A State of the Art Survey
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description: The increasing amount of Linked Data and its inherent distributed nature have attracted significant attention throughout the research community and amongst practitioners to search data, in the past years. Inspired by research results from traditional distributed databases, dierent approaches for managing federation over SPARQL Endpoints have been introduced. SPARQL is the standardised query language for RDF, the default data model used in Linked Data deployments and SPARQL Endpoints are a popular access mechanism provided by many Linked Open Data (LOD) repositories. In this paper, we initially give an overview of the federation framework infrastructure and then proceed with a comparison of existing SPARQL federation frameworks. Finally, we highlight shortcomings in existing frameworks, which we hope helps spawning new research directions.

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issued: 2002-07-13
modified: 2008-01-14

Querying the web of interlinked datasets using void descriptions
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has conclusion: In this paper, we have introduced a query federation engine called WoDQA that discovers related datasets in a VOID store for a query and distributes the query over these datasets. The novelty of our approach is exhaustive dataset selection mechanism which includes analysis of triple pattern relations and links between datasets besides analyzing datasets for each triple pattern. WoDQA focuses on discovering relevant datasets and eliminating irrelevant ones using a rule-based approach introduced in this paper. Our approach requires query the dataset, reflect actual content of the dataset completely and accurately, and include linksets between datasets to select datasets ectively. WoDQA allows users to construct raw queries without the need to know how query will divide into sub-queries and where sub-queries are executed. Query results are complete under the assumption of available, accurate and complete VOID descriptions of datasets. The initial version of WoDQA which is introduced in this paper has some disadvantages arising from query federation approach which WoDQA builds upon. As mentioned previously, follow-your-nose has some problems such as missing results and large document retrieval. Similar problems may occur for query federation. Firstly, to find complete results to queries, it is required that metadata of all datasets must be well-defined and accurate. But, to provide such an accurate dataset metadata an automated mechanism which continuously updates the metadata is required. However, even there would be a tool which implements this requirement, providing accurate dataset metadata via such a tool is the responsibility of dataset publishers. Another problems of query federation are high latency and low selectivity of datasets which are similar to retrieval of large documents in follow-your-nose. Query optimization can be a solution for these problems of query federation. Grouping triple patterns to lter more triples on an endpoint can prevent high latency (…
has conclusion: In this paper, we have introduced a query federation engine calledWoDQA that discovers related datasets in a VOID store for a query and distributes the query over these datasets. The novelty of our approach is exhaustive dataset selection mechanism which includes analysis of triple pattern relations and links between datasets besides analyzing datasets for each triple pattern. WoDQA focuses on discovering relevant datasets and eliminating irrelevant ones using a rule-based approach introduced in this paper. Our approach requires VOID descriptions which include a SPARQL endpoint to query the dataset, reflect actual content of the dataset completely and accurately, and include linksets between datasets to select datasets effectively. WoDQA allows users to construct raw queries without the need to know how query will divide into sub-queries and where sub-queries are executed. Query results are complete under the assumption of available, accurate and complete VOID descriptions of datasets. The initial version of WoDQA which is introduced in this paper has some disadvantages arising from query federation approach which WoDQA builds upon. As mentioned previously, follow-your-nose has some problems such as missing results and large document retrieval. Similar problems may occur for query federation. Firstly, to find complete resultsto queries, it is required that metadata of all datasets must be well-defined and accurate. But, to provide such an accurate dataset metadata an automated mechanism which continuously updates the metadata is required. However, even there would be a tool which implements this requirement, providing accurate dataset metadata via such a tool is the responsibility of dataset publishers. Another problems of query federation are high latency and low selectivity of datasets which are similar to retrieval of large documents in follow-your-nose. Query optimization can be a solution for these problems of query federation. Grouping triple patterns to filte…
has future work: Developing a tool which extracts well-defined VOID descriptions of datasets, and by this means evaluating our approach is a required future work to confirm applicability of WoDQA on linked open data. Also, evaluating the analysis cost of WoDQA for a large VOID store will be possible when well-defined VOIDs are constructed.
propose framework: WoDQA
subject: Querying Distributed RDF Data Sources
creator: Ziya Akar
creator: Oguz Dikenelli
creator: Erdem Eser Ekinci
creator: Tayfun Gökmen Halaç
abstract: Query processing is an important way of accessing data on the Semantic Web. Today, the Semantic Web is characterized as a web of interlinked datasets, and thus querying the web can be seen as dataset integration on the web. Also, this dataset integration must be transparent from the data consumer as if she is querying the whole web. To decide which datasets should be selected and integrated for a query, one requires a metadata of the web of data. In this paper, to enable this transparency, we introduce a federated query engine called WoDQA (Web of Data Query Analyzer) which discovers datasets relevant with a query in an automated manner using VOID documents as metadata. WoDQA focuses on powerful dataset elimination by analyzing query structure with respect to the metadata of datasets. Dataset and linkset descriptions in VOID documents are analyzed for a SPARQL query and a federated query is constructed. By means of linkset concept of VOID, links between datasets are incorporated into selection of federated data sources. Current version of WoDQA is available as a SPARQL endpoint.
publisher: LDOW
title: Querying the Web of Interlinked Datasets using VOID Descriptions
year: 2012

Type Inference on Noisy RDF Data
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creator: Heiko Paulheim
creator: Christian Bizer
abstract: Type information is very valuable in knowledge bases. However, most large open knowledge bases are incomplete with respect to type information, and, at the same time, contain noisy and incorrect data. That makes classic type inference by reasoning difficult. In this paper, we propose the heuristic link-based type inference mechanism SD- Type, which can handle noisy and incorrect data. Instead of leveraging T-box information from the schema, SDType takes the actual use of a schema into account and thus is also robust to misused schema elements.
keywords: Type Inference, Noisy Data, Link-based Classification

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Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs
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creator: Tim Finin
creator: Jennifer Sleeman
keywords: We describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreference resolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are not informative or not known. This problem is similar to coreference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Semantic graphs are semi-structured with very little contextual information and trivial grammars that do not convey additional information. In the absence of known ontologies, performing coreference resolution can be challenging. Our work uses a supervised machine learning algorithm and entity type dictionaries to map attributes to a common attribute space. We evaluated the approach in experiments using data from Wikipedia, Freebase and Arnetminer.

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SPARQL 1.1
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A Schema-Independent Linked Data Interlinking System
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type: RegularPaper
has conclusion: In this paper, we present SLINT, an efficient schema-independent linked data interlinking system. We select important predicates by predicate’s coverage and discriminability. The predicate alignments are constructed and filtered for obtaining key alignments.We implement an adaptive filtering technique to produce candidates and identities. Compare with the most recent systems, SLINT highly outperforms the precision and recall in interlinking. The performance of SLINT is also very high when it takes around 1 minute to detect more than 13,000 identity pairs. Although SLINT has good result on tested datasets, it is not sufficient to evaluate the scalability of our system, which we consider as the current limiting point because of the used of weighted co-occurrence matrix. We will investigate about a solution for this issue in our next work. Besides, we also interested in automatic configuration for every threshold used in SLINT and improving SLINT into a novel cross-domain interlinking system.
propose framework: SLINT
subject: Link Discovery
creator: Bac Le
creator: Ryutaro Ichise
creator: Khai Nguyen
abstract: Linked data interlinking is the discovery of all instances that represent the same real-world object and locate in different data sources. Since different data publishers frequently use different schemas for storing resources, we aim at developing a schema-independent interlinking system. Our system automatically selects important predicates and useful predicate alignments, which are used as the key for blocking and instance matching. The key distinction of our system is the use of weighted co-occurrence and adaptive filtering in blocking and instance matching. Experimental results show that the system highly improves the precision and recall over some recent ones. The performance of the system and the efficiency of main steps are also discussed.
publisher: International Workshop on Ontology Matching
title: SLINT: A Schema-Independent Linked Data Interlinking System
year: 2012

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Dublin Core Metadata Element Set, Version 1.1
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publisher: DCMI

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ReasoningWith Neural Tensor Networks for Knowledge Base Completion
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creator: Richard Socher
creator: Andrew Y.
creator: Christopher D. Manning
creator: Danqi Chen
abstract: Knowledge bases are an important resource for question answering and other tasks but often suffer from incompleteness and lack of ability to reason over their discrete entities and relationships. In this paper we introduce an expressive neural tensor network suitable for reasoning over relationships between two entities. Previous work represented entities as either discrete atomic units or with a single entity vector representation. We show that performance can be improved when entities are represented as an average of their constituting word vectors. This allows sharing of statistical strength between, for instance, facts involving the “Sumatran tiger” and “Bengal tiger.” Lastly, we demonstrate that all models improve when these word vectors are initialized with vectors learned from unsupervised large corpora. We assess the model by considering the problem of predicting additional true relations between entities given a subset of the knowledge base. Our model outperforms previous models and can classify unseen relationships in WordNet and FreeBase with an accuracy of 86.2% and 90.0%, respectively.

D2RQ – Treating Non-RDF Databases as Virtual RDF Graphs
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type: Article
type: RegularPaper
has conclusion: D2RQ offers a flexible, easy-to-use access mechanism to non-RDF databases. It allows the integration of legazy databases into the data access architecture currently standardized by the W3C Data Access Working Group
propose framework: D2RQ
subject: Bringing Relational Databases into the Semantic Web
creator: Christian Bizer
creator: Andy Seaborne
abstract: As Semantic Web technologies are getting mature, there is a growing need for RDF applications to access the content of huge, live, non-RDF, legacy databases without having to replicate the whole database into RDF. In this poster we present D2RQ, a declarative language to describe mappings between application-specific relational database schemata and RDF-S/OWL ontologies. D2RQ allows RDF applications to treat non-RDF relational databases as virtual RDF graphs, which can be queried using RDQL.
publisher: ICDT Workshops
title: D2RQ – Treating Non-RDF Databases as Virtual RDF Graphs
year: 2004

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issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Bringing Relational Databases into the Semantic Web:A Survey
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type: Article
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description: Relational databases are considered one of the most popular storage solutions for various kinds of data and they have been recognized as a key factor in generating huge amounts of data for SemanticWeb applications. Ontologies, on the other hand, are one of the key concepts and main vehicle of knowledge in the Semantic Web research area. The problem of bridging the gap between relational databases and ontologies has attracted the interest of the SemanticWeb community, even from the early years of its existence and is commonly referred to as the database-to-ontology mapping problem. However, this term has been used interchangeably for referring to two distinct problems: namely, the creation of an ontology from an existing database instance and the discovery of mappings between an existing database instance and an existing ontology. In this paper, we clearly define these two problems and present the motivation, benefits, challenges and solutions for each one of them. We attempt to gather the most notable approaches proposed so far in the literature, present them concisely in tabular format and group them under a classification scheme. We finally explore the perspectives and future research steps for a seamless and meaningful integration of databases into the Semantic Web.

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FedX
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type: Approach
type: Framework
address problem: Query Execution
address problem: SPARQL query Federation
address problem: Source Selection
has evaluation: Fedx Evaluation
has GUI: true
implemented in: Java
provide solution: Bind Join
provide solution: Parallelization
provide solution: ASK
support technology: SPARQL 1.0
os: Windows
platform: Sesame

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  on property: canContainHeteronymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: TextChunk
  on property: contains

Source
type: AnnotationProperty
comment: A related resource from which the described resource is derived.
description: The described resource may be derived from the related resource in whole or in part. Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system.
Has Version: source-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

StandardOperatingProcedure
type: Class
subclass of: Entity
subclass of: Abstract
subclass of: Proposition
subclass of: Procedure
subclass of: Restriction
  all values from: Author
  on property: has author
subclass of: Class
  union of: AdminInfoExperiment
  union of: StandardOperatingProcedure
subclass of: Restriction
  all values from: DocumentStage
  on property: has status
subclass of: Class
  union of: ExperimentalMethod
  union of: ProcedureExecuteExperiment
subclass of: Restriction
  all values from: StatusExperimentalDocument
  on property: has status
subclass of: Class
  union of: AdminInfoExperiment
  union of: ProcedureExecuteExperiment
subclass of: Restriction
  cardinality: 1
  on property: has status
subclass of: Restriction
  all values from: RepresentationExperimentalExecutionProcedure
  on property: has representation
subclass of: Restriction
  all values from: PermissionStatus
  on property: has status
subclass of: Class
  union of: ExperimentalGoal
  union of: Model
  union of: ProcedureExecuteExperiment
subclass of: Restriction
  all values from: PlanOfExperimentalActions
  on property: has plan

Putting the spirit of the web back into semantic web querying
type: NamedIndividual
type: RegularPaper
type: Article
has conclusion: In this paper we presented Avalanche , a novel approach for querying the Web of Data that (1) makes no assumptions about data distribution, availability, or partitioning, (2) provides up-to-date results, and (3) is flexible since it assumes nothing about the structure of participating triple stores. Specifically, we showed that Avalanche is able to execute non-trivial queries over distributed data-sources with an ex-ante unknown data-distribution. We showed two possible utility functions to guide the planning and execution over the distributed data-sources—the basic simple model and an extended model exploiting join estimation. We found that whilst the simple model found some results faster it did find less results than the extended model using the same stopping criteria. We believe that if we were to query huge information spaces the overhead of badly selected plans will be subdued by the better but slower plans of the extended utility function. To our knowledge, Avalanche is the first Semantic Web query system that makes no assumptions about the data distribution whatsoever. Whilst it is only a first implementation with a number of drawbacks it represents a first important towards bringing the spirit of the web back to triple-stores—a major condition to fulfill the vision of a truly global and open Semantic Web.
has future work: The Avalanche system has shown how a completely heterogeneous distributed query engine that makes no assumptions about data distribution could be implemented. The current approach does have a number of limitations. In particular, we need to better understand the employed objective functions for the planner, investigate if the requirements put on participating triple-stores are reasonable, explore if Avalanche can be changed to a stateless model, and empirically evaluate if the approach truly scales to large number of hosts. Here we discuss each of these issues in turn. The core optimization of the Avalanche system lies in its cost and utility function. The basic utility function only considers possible joins with no information regarding the probability of the respective join. The proposed utility extension UE estimates the join probability of two highly selective molecules. Although this improves the accuracy of the objective function, its limitation to highly selective molecules is often impractical, as many queries (such as our example query) combine highly selective molecules with non-selective ones. Hence, we need to find a probabilistic distributed join cardinality estimation for low selectivity molecules. One approach might be the usage of bloom-filter caches to store precomputed, “popular” estimates. Another might be investigating sampling techniques for distributed join estimation. In order to support Avalanche existing triple-stores should be able to: – report statistics: cardinalities, bloom filters, other future extensions – support the execution of distributed joins (common in distributed databases), which could be delegated to an intermediary but would be inefficient – share the same key space (can be URIs but would result in bandwidth intensive joins and merges) Whilst these requirements seem simple we need to investigate how complex these extensions of triple-stores are in practice. Even better would be an extension of the SPARQL standard with the ab…
propose framework: Avalanche
subject: Querying Distributed RDF Data Sources
creator: Abraham Bernstein
creator: Cosmin Basca
abstract: Traditionally Semantic Web applications either included a web crawler or relied on external services to gain access to the Web of Data. Recent efforts have enabled applications to query the entire Semantic Web for up-to-date results. Such approaches are based on either centralized indexing of semantically annotated metadata or link traversal and URI dereferencing as in the case of Linked Open Data. By making limiting assumptions about the information space, they violate the openness principle of the Web – a key factor for its ongoing success. In this article we propose a technique called Avalanche, designed to allow a data surfer to query the Semantic Web transparently without making any prior assumptions about the distribution of the data – thus adhering to the openness criteria. Specifically, Avalanche can perform “live” (SPARQL) queries over the Web of Data. First, it gets on-line statistical information about the data distribution, as well as bandwidth availability. Then, it plans and executes the query in a distributed manner trying to quickly provide first answers. The main contribution of this paper is the presentation of this open and distributed SPARQL querying approach. Furthermore, we propose to extend the query planning algorithm with qualitative statistical information. We empirically evaluate Avalanche using a realistic dataset, show its strengths but also point out the challenges that still exist.
publisher: SSWS
title: Avalanche: Putting the Spirit of the Web back into Semantic Web Querying
year: 2010

CoarseError
type: Class
subclass of: ProductRole
subclass of: Error
subclass of: Predicate
subclass of: Role
subclass of: Relation
subclass of: Process-relatedRole
subclass of: Abstract
subclass of: ObservationalError
subclass of: Entity
subclass of: Restriction
  all values from: LevelOfSignificance
  on property: has statistics characteristic
subclass of: Restriction
  all values from: Dispersion
  on property: has statistics characteristic
subclass of: Restriction
  cardinality: 1
  on property: has statistics characteristic
subclass of: Class
  union of: ObservationalError
  union of: ResultError

Description
type: AnnotationProperty
comment: An account of the resource.
description: Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource.
Has Version: description-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

AgreementMaker
type: Framework
type: NamedIndividual
type: Approach
accessed API: SPARQL API
address problem: Filtering
has GUI: true
implemented in: Java
input type: SPARQL
provide solution: indexing
platform: Jena

ExperimentalProtocol
type: Class
subclass of: Procedure
subclass of: Entity
subclass of: Abstract
subclass of: Proposition
subclass of: Restriction
  all values from: DocumentStage
  on property: has status
subclass of: Restriction
  cardinality: 1
  on property: has status
subclass of: Restriction
  all values from: PlanOfExperimentalActions
  on property: has plan
subclass of: Restriction
  all values from: PermissionStatus
  on property: has status
subclass of: Class
  union of: AdminInfoExperiment
  union of: ProcedureExecuteExperiment
subclass of: Class
  union of: ExperimentalMethod
  union of: ProcedureExecuteExperiment
subclass of: Restriction
  all values from: RepresentationExperimentalExecutionProcedure
  on property: has representation
subclass of: Class
  union of: ExperimentalGoal
  union of: Model
  union of: ProcedureExecuteExperiment
subclass of: Restriction
  all values from: StatusExperimentalDocument
  on property: has status

Accessing and Documenting Relational Databases through OWL Ontologies
type: Publication
type: RegularPaper
type: Article
type: SingleAuthorPublication
type: NamedIndividual
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
has conclusion: In this paper we presented a completely automated approach to map relational databases and ontologies. The system proposed is capable of extracting an ontological view of the relational schema, and to enable SPARQL access to the relational data source by means of a query rewriting mechanism. The same approach can be used to efficiently store relational ontologies on a RDBMS; moreover, the mapping we devised is completely based on OWL with no need to resort to a new formalism. The impact of this system has been discussed considering three main applications: (i) publishing of relational data in an ontological format, (ii) documentation of relational schemas by means of ontological annotations, and (iii) efficient relational storage for data-intensive ontologies
propose framework: ROSEX
subject: Bringing Relational Databases into the Semantic Web
creator: Giorgio Orsi
creator: Letizia Tanca
creator: Emanuele Panigati
creator: Carlo Curino
abstract: Relational databases have been designed to store high volumes of data and to provide an efficient query interface. Ontologies are geared towards capturing domain knowledge, annotations, and to offer high level, machine-processable views of data and metadata. The complementary strengths and weaknesses of these data models motivate the research effort we present in this paper. The goal of this work is to bridge the relational and ontological worlds, in order to leverage the efficiency and scalability of relational technologies and the high level view of data and metadata proper of ontologies. The system we designed and developed achieves: (i) automatic ontology extraction from relational data sources and (ii) automatic query translation from SPARQL to SQL. Among the others, we focus on two main applications of this novel technology: (i) ontological publishing of relational data, and (ii) automatic relational schema annotation and documentation. The system has been designed and tested against real life scenarios from Big Science projects, which are used as running examples throughout the paper.
publisher: FQAS
title: Accessing and Documenting Relational Databases through OWL Ontologies
year: 2009

Class
subclass of: Container
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

AuthorProtocol
type: Class
subclass of: Entity
subclass of: ActorRole
subclass of: Abstract
subclass of: Role
subclass of: Relation
subclass of: Action-relatedRole
subclass of: Predicate
subclass of: SubjectRole
subclass of: Restriction
  cardinality: 1
  on property: has admin info
subclass of: Restriction
  all values from: AdminInfoAuthor
  on property: has admin info

SubjectOfAction
type: Class
subclass of: SubjectRole
subclass of: ActorRole
subclass of: Role
subclass of: Entity
subclass of: Abstract
subclass of: Action-relatedRole
subclass of: Predicate
subclass of: Relation
subclass of: Restriction
  cardinality: 1
  on property: has admin info
subclass of: Restriction
  all values from: AdminInfoSubjectOfExperiment
  on property: has admin info

One-factorExperiment
type: Class
subclass of: ClassificationOfExperiments
subclass of: Proposition
subclass of: Entity
subclass of: Classification
subclass of: ScientificTask
subclass of: Abstract
subclass of: Restriction
  cardinality: 1
  on property: has number of factors
subclass of: Class
  union of: ClassificationOfDomains
  union of: ClassificationOfExperiments
subclass of: Restriction
  all values from: Number
  on property: has number of factors

Creator
type: ObjectProperty
comment: An entity primarily responsible for making the resource.
domain: Publication
domain: Restriction
  all values from: Publication
  on property: cite
range: Agent
range: Person
range: SpatialThing
description: Examples of a Creator include a person, an organization, or a service. Typically, the name of a Creator should be used to indicate the entity.
Has Version: creator-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

StatisticalError
type: Class
subclass of: Entity
subclass of: Role
subclass of: Process-relatedRole
subclass of: Predicate
subclass of: Error
subclass of: ObservationalError
subclass of: Relation
subclass of: ProductRole
subclass of: MeasurementError
subclass of: Abstract
subclass of: Restriction
  cardinality: 1
  on property: has statistics characteristic
subclass of: Restriction
  all values from: Dispersion
  on property: has statistics characteristic
subclass of: Restriction
  all values from: LevelOfSignificance
  on property: has statistics characteristic
subclass of: Class
  union of: ObservationalError
  union of: ResultError

has target variable
type: ObjectProperty
domain: Abstract
domain: Proposition
domain: Entity
domain: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
domain: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
domain: Restriction
  all values from: TargetVariable
  on property: has target variable
domain: Restriction
  cardinality: 1
  on property: has complexity
domain: Restriction
  cardinality: 1
  on property: has explicitness
domain: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
domain: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
domain: Restriction
  cardinality: 1
  on property: has generality
range: Variable
range: Proposition
range: Abstract
range: Entity

Adaptive Integration of Distributed Semantic Web Data
type: NamedIndividual
type: SingleAuthorPublication
type: Article
type: RegularPaper
type: Publication
type: Restriction
  all values from: Person
  on property: author
type: Restriction
  all values from: Publication
  on property: cite
has conclusion: An adaptive framework has been presented for executing queries over multiple SPARQL endpoints that differs from existing approaches which use static query optimisation techniques. Many SPARQL web services are currently available and the number of them is growing. The work presented in this paper is a framework for executing queries over federations of such services. The framework proposed in this paper, which allows adaptive query processing over dynamically constructed predicate tables to be performed in conjunction with the construction of the predicate tables, was shown to perform relatively well in unpredictable environments where source query failures may occur. The prototype implemented was evaluated using real data, showing some advantage in terms of response times of adaptive over non-adaptive methods using a subset of DBPedia. Future work will aim to investigate other data sets with different characteristics and 192 S. Lynden et al. larger data sets. As the approach presented in this paper focuses on efficiently executing a specific kind of query, that of adaptively ordering multiple joins, further work will focus on optimising other kinds of queries and implementing support for more SPARQL query language features. Future work will also concentrate on investigating how the work can be applied in various domains.
has conclusion: The use of RDF (Resource Description Framework) data is a cornerstone of the Semantic Web. RDF data embedded in Web pages may be indexed using semantic search engines, however, RDF data is often stored in databases, accessible viaWeb Services using the SPARQL query language for RDF, which form part of the Deep Web which is not accessible using search engines. This paper addresses the problem of effectively integrating RDF data stored in separate Web-accessible databases. An approach based on distributed query processing is described, where data from multiple repositories are used to construct partitioned tables that are integrated using an adaptive query processing technique supporting join reordering, which limits any reliance on statistics and metadata about SPARQL endpoints, as such information is often inaccurate or unavailable, but is required by existing systems supporting federated SPARQL queries. The approach presented extends existing approaches in this area by allowing tables to be added to the query plan while it is executing, and shows how an approach currently used within relational query processing can be applied to distributed SPARQL query processing. The approach is evaluated using a prototype implementation and potential applications are discussed.
has future work: Future work will also concentrate on investigating how the work can be applied in various domains.
has future work: Future work will aim to investigate other data sets with different characteristics and larger data sets. As the approach presented in this paper focuses on efficiently executing a specific kind of query, that of adaptively ordering multiple joins, further work will focus on optimising other kinds of queries and implementing support for more SPARQL query language features. Future work will also concentrate on investigating how the work can be applied in various domains.
propose framework: ADERIS
subject: Querying Distributed RDF Data Sources
creator: Isao Kojima
creator: Akiyoshi Matono
creator: Yusuke Tanimura
creator: Steven Lynden
abstract: The use of RDF (Resource Description Framework) data is a cornerstone of the Semantic Web. RDF data embedded in Web pages may be indexed using semantic search engines, however, RDF data is often stored in databases, accessible viaWeb Services using the SPARQL query language for RDF, which form part of the Deep Web which is not accessible using search engines. This paper addresses the problem of effectively integrating RDF data stored in separate Web-accessible databases. An approach based on distributed query processing is described, where data from multiple repositories are used to construct partitioned tables that are integrated using an adaptive query processing technique supporting join reordering, which limits any reliance on statistics and metadata about SPARQL endpoints, as such information is often inaccurate or unavailable, but is required by existing systems supporting federated SPARQL queries. The approach presented extends existing approaches in this area by allowing tables to be added to the query plan while it is executing, and shows how an approach currently used within relational query processing can be applied to distributed SPARQL query processing. The approach is evaluated using a prototype implementation and potential applications are discussed.
publisher: International Workshop on Databases in Networked Information Systems
title: Adaptive Integration of Distributed Semantic Web Data
year: 2010

DCMI Box
type: NamedIndividual
type: Datatype
comment: The set of regions in space defined by their geographic coordinates according to the DCMI Box Encoding Scheme.
Has Version: Box-003
issued: 2000-07-11
modified: 2008-01-14

Class
subclass of: Restriction
  has value: false
  on property: canContainHomonymousElements
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

Fedx Evaluation
type: Evaluation
type: Benchmark
type: DiscourseElement
type: NamedIndividual
follows evaluation method: FedxEvalMethod
has experiment setup: All experiments are carried out on an HP Proliant DL360 G6 with 2GHz 4Core CPU with 128KB L1 Cache, 1024KB L2 Cache, 4096KB L3 Cache, 32GB 1333MHz RAM, and a 160 GB SCSI hard drive. A 64bit Windows 2008 Server operating system and the 64bit Java VM 1.6.0 22 constitute the software environment.
has result: fedx results
has input: Cross Domain
has input: Life Science

Class
subclass of: Container
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

Avalanche Evaluation
type: Evaluation
type: NamedIndividual
has experiment setup: We tested Avalanche using a five-node cluster. Each machine had 2GB RAM and an Intel Core 2 Duo E8500
uses information asset: IEEE
uses information asset: DBLP
uses information asset: ACM

AuthorSOP
type: Class
subclass of: Entity
subclass of: Role
subclass of: Abstract
subclass of: Relation
subclass of: ActorRole
subclass of: Action-relatedRole
subclass of: Predicate
subclass of: SubjectRole
subclass of: Restriction
  all values from: AdminInfoAuthor
  on property: has admin info
subclass of: Restriction
  cardinality: 1
  on property: has admin info

RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems
type: Article
type: NamedIndividual
type: RegularPaper
has conclusion: In this paper, an algorithm and tool for generating OWL ontologies from relational database systems is presented. The tool, RDB2ONT, helps the domain experts to quickly generate and publish OWL ontologies describing the underlying relational database systems while preserving their structural constraints. The generated ontologies are constructed using a set of vocabularies and structures defined in schema that describes relational database systems on the web so they guarantees that user applications can work with data instances that conformed to a set of known vocabularies and structures. The generated ontologies provide a standardized and meaningful way for describing the underlying relational database systems so they “bridge” the semantic gaps between the ontologies describing relational database systems and/or the ontologies describing other data sources on the web such as flat-files, semi-structures, etc. Concepts in OWL ontologies can be defined at multiple levels of granularities thus the generated OWL ontologies can be used to address the semantic heterogeneity problem at multiple levels. Evolutions of database systems in large-scale environments are inevitable so by using the RDB2ONT tool, OWL ontologies can be re-generated with little effort from the domain experts thus speed up the process of facilitating data in the underlying relational database systems with other data sources on the web.
has future work: Although the generated OWL ontologies provide the explicit meaning of concepts and their semantic relationships between related concepts, there are still many open research questions that need to be addressed. One of the questions is how to merge the generated OWL ontologies into an integrated OWL ontology so that common views of concepts can be achieved? This would allow users to pose queries on the common views of concepts rather than the concepts defined in the individual ontologies. Merging OWL ontologies will also require the resolution of the structural differences between OWL ontologies. Another question is how can existing OWL ontology inference engines such as Jena 2 or Pellet can be used to infer the semantic relationships between concepts defined in multiple OWL ontologies at multiple levels of granularities? The result of this research is important since it would enable users to combine related data from multiple data sources at multiple levels of granularities. In the real world, different organizations are likely to use different data models, thus tools for generating OWL ontologies automatically and dynamically from other data models (e.g., flat-files, object-oriented, etc.) are also needed. As mentioned previously, having different OWL ontologies describing different data models will allow users to relate semantically related data in different data models together thus enabling these different data sources to interoperate with each other semantically.
propose framework: RDB2ONT
subject: Bringing Relational Databases into the Semantic Web
creator: Reda Alhajj
creator: Ken Barker
creator: Quang Trinh
abstract: This paper describes a framework that uses the Semantic Web infrastructure to address semantic interoperability between relational database systems in large-scale environments and at multiple levels of granularities. Given a relational database system, we describe a formal algorithm to use the relational database Rs meta-data and structural constraints to construct its OWL ontology while preserving the structural constraints of the underlying relational database system. The generated ontology is described using and conforming to a set of vocabularies defined in an ontology that describes relational database systems on the web. Using this set of vocabularies guarantee that applications on the web can work with data instances that conformed to a set of known vocabularies and structures. Finally, we describe our prototype and how semantic conflicts are resolved between multiple relational database systems using the generated ontologies
publisher: AICTICIW
title: RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems
year: 2006

Accrual Method
type: NamedIndividual
type: ObjectProperty
comment: The method by which items are added to a collection.
domain: Collection
range: MethodOfAccrual
Has Version: accrualMethod-003
issued: 2005-06-13
modified: 2010-10-11

decomposition approach
type: EvaluationMethod
type: Approach
type: NamedIndividual
has description: decomposition approach that relies on exclusive groups
has implementation: FedX

has name
type: ObjectProperty
domain: Physical
domain: Object
domain: ExecutionOfExperiment
domain: ScientificActivity
domain: Process
domain: Agent
domain: Restriction
  all values from: ExperimentalMethod
  on property: has method
domain: Restriction
  all values from: TimePoint
  on property: has timing
domain: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
domain: Restriction
  all values from: ExperimentalEquipment
  on property: has instrument
domain: Restriction
  all values from: ActionGoal
  on property: has goal
domain: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
  union of: ScientificExperiment
domain: Restriction
  all values from: ActionComplexity
  on property: has action complexity
domain: Restriction
  cardinality: 1
  on property: has action complexity
domain: Restriction
  all values from: ActionName
  on property: has name
range: Abstract
range: Attribute
range: Name
range: Entity

License Document
type: NamedIndividual
type: Class
comment: A legal document giving official permission to do something with a Resource.
subclass of: RightsStatement
Has Version: LicenseDocument-001
issued: 2008-01-14

has EXPO concept
type: ObjectProperty
domain: Role
domain: Entity
domain: Relation
domain: AttributeRole
domain: Predicate
domain: Abstract
domain: Restriction
  all values from: Name
  on property: has EXPO concept
domain: Restriction
  cardinality: 1
  on property: has EXPO concept
domain: Restriction
  all values from: Name
  on property: has synonymousExternalConcept
range: Abstract
range: Entity
range: Attribute

Class
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

semsur
type: Imp
is rule enabled: true
body: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#p
  argument2: http://purl.org/SemSur/SemSur#x
  property predicate: Creator
body: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#p
  argument2: http://purl.org/SemSur/SemSur#y
  property predicate: Creator
body: SameIndividualAtom
  argument1: http://purl.org/SemSur/SemSur#x
  argument2: http://purl.org/SemSur/SemSur#y
body: ClassAtom
  argument1: http://purl.org/SemSur/SemSur#p
  class predicate: Publication
head: ClassAtom
  argument1: http://purl.org/SemSur/SemSur#p
  class predicate: SingleAuthorPublication

has method
type: ObjectProperty
domain: Physical
domain: Object
domain: Process
domain: ExecutionOfExperiment
domain: ScientificActivity
domain: Agent
domain: Restriction
  all values from: ExperimentalEquipment
  on property: has instrument
domain: Restriction
  all values from: ActionComplexity
  on property: has action complexity
domain: Restriction
  all values from: TimePoint
  on property: has timing
domain: Restriction
  all values from: ExperimentalMethod
  on property: has method
domain: Restriction
  all values from: ActionGoal
  on property: has goal
domain: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
  union of: ScientificExperiment
domain: Restriction
  cardinality: 1
  on property: has action complexity
domain: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
domain: Restriction
  all values from: ActionName
  on property: has name
range: Abstract
range: Proposition
range: Entity
range: Procedure
range: ResearchMethod
range: Restriction
  all values from: PlanOfExperimentalActions
  on property: has plan
range: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
range: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
  union of: ScientificExperiment
range: Class
  union of: ExperimentalMethod
  union of: ProcedureExecuteExperiment
range: Restriction
  all values from: MethodApplicabilityCondition
  on property: has applicability
range: Restriction
  all values from: MethodGoal
  on property: has goal
range: Restriction
  all values from: SubjectMethod
  on property: has subject

has number of factors
type: ObjectProperty
domain: ScientificTask
domain: ClassificationOfExperiments
domain: Abstract
domain: Classification
domain: Proposition
domain: Entity
domain: Class
  union of: ClassificationOfDomains
  union of: ClassificationOfExperiments
domain: Restriction
  all values from: Number
  on property: has number of factors
domain: Restriction
  cardinality: 1
  on property: has number of factors
range: Entity
range: Abstract
range: Quantity

Zhishi.links Results for OAEI 2011
type: SingleAuthorPublication
type: Publication
type: Article
type: RegularPaper
type: NamedIndividual
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
has conclusion: In this report, we have presented a brief description of Zhishi.links, an instance matching system. We have introduced the architecture of our system and specific techniques we used. Also, the results have been analyzed in detail and several guides for improvements have been proposed. We look forwards to build an instance matching system with better performance and higher stability in the future.
has future work: We look forwards to build an instance matching system with better performance and higher stability in the future.
propose framework: Zhishi.links
subject: Link Discovery
creator: Yunlong Zhang
creator: Haofen Wang
creator: Shu Rong
creator: Xing Niu
abstract: This report presents the results of Zhishi.links, a distributed instance matching system, for this year’s Ontology Alignment Evaluation Initiative (OAEI) campaign. We participate in Data Interlinking track (DI) of IM
title: Zhishi.links Results for OAEI 2011
year: 2011

Class
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: ListOfReferences
intersection of: Restriction
  on property: contains
  some values from: BibliographicReference

LogicalModelRepresentation
type: Class
subclass of: Agent
subclass of: ContentBearingPhysical
subclass of: Representation
subclass of: Physical
subclass of: Object
subclass of: ContentBearingObject
subclass of: Restriction
  cardinality: 1
  on property: has representation style
subclass of: Restriction
  all values from: RepresentationForm
  on property: has representation style
subclass of: Restriction
  all values from: Proposition
  on property: has content
subclass of: Class
  union of: BiblioReference
  union of: ModelRepresentation
  union of: RepresentationExperimentalExecutionProcedure
  union of: RepresentationExperimentalGoal
subclass of: Restriction
  cardinality: 1
  on property: has content

ADERIS
type: Framework
type: NamedIndividual
type: Approach
address problem: Source Selection
address problem: SPARQL query Federation
address problem: Query Execution
has GUI: true
implemented in: Java
provide solution: Nested Loop Join
provide solution: Predicate List
support technology: SPARQL 1.0

Class
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

Abstract
type: DatatypeProperty
type: NamedIndividual
type: AnnotationProperty
comment: A summary of the resource.
sub property of: description
sub property of: Description
Has Version: abstract-003
issued: 2000-07-11
modified: 2008-01-14

Extent
type: NamedIndividual
type: ObjectProperty
type: AnnotationProperty
comment: The size or duration of the resource.
range: Size or Duration
sub property of: Format
sub property of: format
Has Version: extent-003
issued: 2000-07-11
modified: 2008-01-14

Browsing Linked Open Data with Auto Complete
type: NamedIndividual
type: Article
type: SingleAuthorPublication
type: Publication
type: RegularPaper
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
creator: Heiko Paulheim
abstract: Information in Linked Open Data is incomplete by nature and design. Nevertheless, the more complete information delivered on a subject is, the higher is its value to an end user. With our submission to the Semantic Web Challenge1, we show how information, in particular types, can be completed automatically, based on heuristic rule learning. We introduce an approach which employs lazy learning instead of learning a global model, making the approach well scalable to large data, while at the same time providing results at an accuracy of 85.6%. The auto complete function is included in a modular semantic web browser.
keywords: Linked Open Data, Automatic Completion, Association Rule Mining, Ontology Learning, Ontology Alignment, Lazy Learning

Semsur3
type: Imp
comment: subproblem has the motivation of the problem
is rule enabled: true
body: ClassAtom
  argument1: http://purl.org/SemSur/SemSur#x
  class predicate: Problem
body: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#y
  argument2: http://purl.org/SemSur/SemSur#m
  property predicate: hasMotivation
body: ClassAtom
  argument1: http://purl.org/SemSur/SemSur#y
  class predicate: Problem
body: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#x
  argument2: http://purl.org/SemSur/SemSur#y
  property predicate: isSubproblem
head: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#x
  argument2: http://purl.org/SemSur/SemSur#m
  property predicate: hasMotivation

File Format
type: Class
type: NamedIndividual
comment: A digital resource format.
subclass of: MediaType
subclass of: MediaTypeOrExtent
description: Examples include the formats defined by the list of Internet Media Types.
Has Version: FileFormat-001
issued: 2008-01-14

Class
subclass of: Restriction
  has value: true
  on property: canContainHomonymousElements
intersection of: Table
intersection of: Restriction
  on property: contains
  some values from: Pattern

Has Format
type: ObjectProperty
type: NamedIndividual
type: AnnotationProperty
comment: A related resource that is substantially the same as the pre-existing described resource, but in another format.
sub property of: Relation
Has Version: hasFormat-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

has author
type: ObjectProperty
range: Abstract
range: Role
range: Action-relatedRole
range: Relation
range: SubjectRole
range: Entity
range: Predicate
range: ActorRole
range: Restriction
  cardinality: 1
  on property: has admin info
range: Restriction
  all values from: AdminInfoAuthor
  on property: has admin info

Splendid evaluation
type: Evaluation
type: NamedIndividual
has experiment setup: Due to the unpredictable availability and latency of the original SPARQL endpoints of the benchmark dataset we used local copies of them which were hosted on five 64bit Intel(R) Xeon(TM) CPU 3.60GHz server instances running Sesame 2.4.2 with each instance providing the SPARQL endpoint for one life science and for one cross domain dataset. The evaluation was performed on a separate server instance with 64bit Intel(R) Xeon(TM) CPU 3.60GHz and a 100Mbit network connection.
uses information asset: SP2B
uses information asset: BSBM

Semsur2
type: Imp
is rule enabled: true
body: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#p
  argument2: http://purl.org/SemSur/SemSur#x
  property predicate: Creator
body: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#p
  argument2: http://purl.org/SemSur/SemSur#y
  property predicate: Creator
body: ClassAtom
  argument1: http://purl.org/SemSur/SemSur#p
  class predicate: Publication
body: DifferentIndividualsAtom
  argument1: http://purl.org/SemSur/SemSur#x
  argument2: http://purl.org/SemSur/SemSur#y
head: IndividualPropertyAtom
  argument1: http://purl.org/SemSur/SemSur#y
  argument2: http://purl.org/SemSur/SemSur#x
  property predicate: isCoAuthor

Date Submitted
type: NamedIndividual
type: DatatypeProperty
type: AnnotationProperty
comment: Date of submission of the resource.
range: Literal
sub property of: date
description: Examples of resources to which a Date Submitted may be relevant are a thesis (submitted to a university department) or an article (submitted to a journal).
Has Version: dateSubmitted-002
issued: 2002-07-13
modified: 2008-01-14

Is Referenced By
type: ObjectProperty
type: AnnotationProperty
type: NamedIndividual
comment: A related resource that references, cites, or otherwise points to the described resource.
sub property of: Relation
Has Version: isReferencedBy-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

has timing
type: ObjectProperty
domain: ExecutionOfExperiment
domain: ScientificActivity
domain: Process
domain: Object
domain: Agent
domain: Physical
domain: Restriction
  all values from: ActionName
  on property: has name
domain: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
domain: Restriction
  all values from: ActionComplexity
  on property: has action complexity
domain: Restriction
  all values from: ExperimentalMethod
  on property: has method
domain: Restriction
  cardinality: 1
  on property: has action complexity
domain: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
  union of: ScientificExperiment
domain: Restriction
  all values from: ActionGoal
  on property: has goal
domain: Restriction
  all values from: TimePoint
  on property: has timing
domain: Restriction
  all values from: ExperimentalEquipment
  on property: has instrument
range: Abstract
range: TimeMeasure
range: Quantity
range: Entity
range: PhysicalQuantity
range: TimePosition

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: List
intersection of: Restriction
  all values from: Reference
  on property: contains

HypothesisAcceptanceMistake
type: Class
subclass of: Entity
subclass of: Relation
subclass of: Process-relatedRole
subclass of: Abstract
subclass of: Predicate
subclass of: Error
subclass of: ProductRole
subclass of: Role
subclass of: Restriction
  cardinality: 1
  on property: has statistics characteristic
subclass of: Class
  union of: ObservationalError
  union of: ResultError
subclass of: Restriction
  all values from: LevelOfSignificance
  on property: has statistics characteristic
subclass of: Restriction
  all values from: Dispersion
  on property: has statistics characteristic

RFC 5646
type: NamedIndividual
type: Datatype
comment: The set of tags constructed according to RFC 5646 for the identification of languages.
description: RFC 5646 obsoletes RFC 4646.
Has Version: RFC5646-001
issued: 2010-10-11

NullHypothesis
type: Class
subclass of: Abstract
subclass of: Entity
subclass of: Proposition
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  cardinality: 1
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation

Type
type: NamedIndividual
type: AnnotationProperty
type: ObjectProperty
comment: The nature or genre of the resource.
range: Class
sub property of: Type
description: Recommended best practice is to use a controlled vocabulary such as the DCMI Type Vocabulary [DCMITYPE]. To describe the file format, physical medium, or dimensions of the resource, use the Format element.
Has Version: typeT-001
issued: 2008-01-14
modified: 2008-01-14

The Semantic Survey Ontology (semsur)
type: Ontology
license: http://creativecommons.org/licenses/by/3.0/
Contributor: foaf.rdf
creator: Sahar Vahdati
creator: Said Fathalla
creator: Soeren Auer
creator: Christoph Lange
description: SemSur, the Semantic Survey Ontology, is a core ontology for describing individual research problems, approaches, implementations and evaluations in a structured, comparable way.
rights: Copyright 2018
issued: 2018-01-08
modified: 2018-04-11
title: SemSur, Version 2.0
preferred namespace prefix: semsur
imports: swrla.owl
imports: deo
imports: doco
imports: core
imports: mls#
imports: http://purl.org/spar/fabio/2017-05-29
imports: swrl#
imports: http://purl.org/spar/doco/2015-07-03
imports: EXPOApr19.xml
imports: ontology
prior version: 1.0
version IRI: http://purl.org/SemSur/2.0
version info: 2.0

LIMES evaluation
type: Evaluation
type: NamedIndividual
has experiment setup: Every time measurement experiment was carried out as a single thread on a 32-bit system with a 2.5GHz Intel Core Duo CPU and 4GB RAM. For our experiments, we used version 0.3.2 of LIMES and version 2.0 of SILK.
uses information asset: DBpedia

DARQEvalMethod
type: EvaluationMethod
type: NamedIndividual
has description: Evaluate the performance of the DARQ query engine b y running four example queries and evaluated the runtime with and without optimization.
has evaluation dimension: Runtime performance

Distributed_SPARQL
type: NamedIndividual
type: Framework
type: Approach
address problem: Query Execution
address problem: SPARQL query Federation
address problem: Source Selection
has GUI: false
implemented in: Java
provide solution: Bind Join
support technology: SPARQL 1.0
platform: Sesame

Motivation of SPARQL query Federation
type: Motivation
type: DiscourseElement
type: EvaluationMethod
type: NamedIndividual
has description: unified query processing over RDF data sources accessible via SPARQL endpoints
motivates approach: decomposition approach
motivates approach: adaptive query processing approach

Improving the Quality of Linked Data Using Statistical Distributions
type: Article
type: NamedIndividual
type: Publication
type: SingleAuthorPublication
type: RegularPaper
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
creator: Heiko Paulheim
creator: Christian Bizer
abstract: Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.
keywords: Data Quality, Type Completion, Error Detection, Noisy Data, Semi-Structured Data, DBpedia, NELL

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: TextChunk
  on property: contains

ImproperSampling
type: Class
subclass of: Relation
subclass of: ProductRole
subclass of: Abstract
subclass of: Error
subclass of: Process-relatedRole
subclass of: Entity
subclass of: Role
subclass of: Predicate
subclass of: Restriction
  all values from: LevelOfSignificance
  on property: has statistics characteristic
subclass of: Restriction
  cardinality: 1
  on property: has statistics characteristic
subclass of: Restriction
  all values from: Dispersion
  on property: has statistics characteristic
subclass of: Class
  union of: ObservationalError
  union of: ResultError

BibliographicReferenceList
type: Class
subclass of: Table
subclass of: Pattern
subclass of: Structured
subclass of: Bucket
subclass of: NonTextual
subclass of: DiscourseElement
subclass of: ListOfReferences
subclass of: Container
subclass of: List
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Field
    union of: Class
      intersection of: Container
      intersection of: Class
        complement of: Class
  on property: contains
subclass of: Restriction
  on property: contains
  some values from: Pattern
subclass of: Restriction
  all values from: Reference
  on property: contains
subclass of: Restriction
  on property: Has Part
  some values from: Reference
subclass of: Class
  intersection of: Table
  intersection of: Restriction
    on property: contains
    some values from: Pattern
subclass of: Restriction
  on property: contains
  some values from: BibliographicReference
subclass of: Class
  union of: HeadedContainer
  union of: Table
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Container
    union of: Field
    union of: Meta
  on property: contains
subclass of: Class
  intersection of: List
  intersection of: Restriction
    all values from: Reference
    on property: contains
subclass of: Restriction
  has value: true
  on property: canContainHomonymousElements
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta
subclass of: Class
  union of: Container
  union of: Popup
subclass of: Class
  complement of: Textual
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
subclass of: Class
  union of: Block
  union of: Container
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
subclass of: Restriction
  on property: Has Part
  some values from: BibliographicReference

Enriching DBpedia with Targeted Hypernym Discovery
type: Publication
type: Article
type: SingleAuthorPublication
type: NamedIndividual
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
description: The Linked Hypernyms Dataset (LHD) provides entities described by Dutch, English and German Wikipedia articles with types in the DBpedia namespace. The types are extracted from the first sentences of Wikipedia articles using Hearst pattern matching over part-of-speech annotated text and disambiguated to DBpedia concepts. The dataset covers 1.3 million RDF type triples from English Wikipedia, out of which 1 million RDF type triples were found not to overlap with DBpedia, and 0.4 million with YAGO2s. There are about 770 thousand German and 650 thousand Dutch Wikipedia entities assigned a novel type, which exceeds the number of entities in the localized DBpedia for the respective language. RDF type triples from the German dataset have been incorporated to the German DBpedia. Quality assessment was performed altogether based on 16.500 human ratings and annotations. For the English dataset, the average accuracy is 0.86, for German 0.77 and for Dutch 0.88. The accuracy of raw plain text hypernyms exceeds 0.90 for all languages. The LHD release described and evaluated in this article targets DBpedia 3.8, LHD version for the DBpedia 3.9 containing approximately 4.5 million RDF type triples is also available.

Identifier
type: AnnotationProperty
comment: An unambiguous reference to the resource within a given context.
description: Recommended best practice is to identify the resource by means of a string conforming to a formal identification system.
Has Version: identifier-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

MeSH
type: VocabularyEncodingScheme
type: NamedIndividual
comment: The set of labeled concepts specified by the Medical Subject Headings.
Has Version: MESH-003
issued: 2000-07-11
modified: 2008-01-14

Location
type: NamedIndividual
type: Class
comment: A spatial region or named place.
subclass of: Location, Period, or Jurisdiction
Has Version: Location-001
issued: 2008-01-14

ExperimenteeBias
type: Class
subclass of: Abstract
subclass of: Entity
subclass of: Proposition
subclass of: ExperimentalHypothesis
subclass of: AlternativeHypothesis
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  cardinality: 1
  on property: has complexity
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality

AgreementMaker: Ecient Matching for Large Real-World Schemas and Ontologies
type: NamedIndividual
type: Article
type: RegularPaper
has conclusion: Our demo focuses on the matching methods and evaluation strategies for determining the efficiency of ontology matching methods. Due to the tight integration of the evaluation strategies with the graphical user interface, a unique feature of our system, all the steps will be performed through the interface. Users will start by uploading their own ontologies, load our own, or download ontologies from the web, thus taking advantage of the several standard formats supported. Users can then explore the interface freely or follow a walk-through, consisting of browsing the ontologies, expanding and contracting nodes, and customizing the display. They have access to the information associated with each concept to be aligned, including descriptions, annotations, and (context) relations, and they can use them to visually detect mappings. The matching methods offer a brief description to help users in their selection. Users can manually set parameters like threshold or use default values. Once the mappings are defined, they will be able to interact with the results obtained, for example, to reduce the number of lines depicting the mappings. They can also edit the mappings that are automatically produced by the system, by adding, deleting, and updating mappings. Several matchings can be viewed and managed in parallel to directly detect overlaps and differences visually. Tuning the performance of the algorithms becomes an easy and quick task: for example, mappings can be iteratively improved by running second layer matchers, such as DSI after one of the base similarity methods. In the end, a nal set of mappings can be obtained using basic combination operations or an automatic quality composition of previous matchings. Users will also be able to store and inspect the mappings thus deffned. The system also provides a powerful framework for developers. Each matching method is implemented as a standard component which allows reuse and composition. A standard extensible API is defin…
propose framework: AgreementMaker
subject: Link Discovery
creator: Flavio Palandri Antonelli
creator: Cosmin Stroe
creator: Isabel F. Cruz
publisher: VLDB
title: AgreementMaker: Ecient Matching for Large Real-World Schemas and Ontologies
year: 2009

Two-factorExperiment
type: Class
subclass of: Abstract
subclass of: Classification
subclass of: ClassificationOfExperiments
subclass of: Entity
subclass of: Proposition
subclass of: ScientificTask
subclass of: Class
  union of: ClassificationOfDomains
  union of: ClassificationOfExperiments
subclass of: Restriction
  cardinality: 1
  on property: has number of factors
subclass of: Restriction
  all values from: Number
  on property: has number of factors

Instructional Method
type: NamedIndividual
type: ObjectProperty
comment: A process, used to engender knowledge, attitudes and skills, that the described resource is designed to support.
range: Method of Instruction
description: Instructional Method will typically include ways of presenting instructional materials or conducting instructional activities, patterns of learner-to-learner and learner-to-instructor interactions, and mechanisms by which group and individual levels of learning are measured. Instructional methods include all aspects of the instruction and learning processes from planning and implementation through evaluation and feedback.
Has Version: instructionalMethod-002
issued: 2005-06-13
modified: 2008-01-14

GDS
type: Approach
type: Framework
type: NamedIndividual
address problem: Query Execution
address problem: Source Selection
address problem: SPARQL query Federation
has GUI: false
provide solution: Bind Join
provide solution: Semi Join
support technology: SPARQL 1.0
platform: Jena

Class
subclass of: Class
  union of: Block
  union of: Container
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

has complexity
type: ObjectProperty
domain: Abstract
domain: Proposition
domain: Entity
domain: Restriction
  cardinality: 1
  on property: has generality
domain: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
domain: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
domain: Restriction
  cardinality: 1
  on property: has explicitness
domain: Restriction
  cardinality: 1
  on property: has complexity
domain: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
domain: Restriction
  all values from: TargetVariable
  on property: has target variable
domain: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
range: Attribute
range: AttributeOfHypothesis
range: Abstract
range: Entity

MeasurementError
type: Class
subclass of: Role
subclass of: Process-relatedRole
subclass of: Error
subclass of: ProductRole
subclass of: Abstract
subclass of: Relation
subclass of: Entity
subclass of: Predicate
subclass of: Restriction
  all values from: LevelOfSignificance
  on property: has statistics characteristic
subclass of: Restriction
  all values from: Dispersion
  on property: has statistics characteristic
subclass of: Class
  union of: ObservationalError
  union of: ResultError
subclass of: Restriction
  cardinality: 1
  on property: has statistics characteristic

Is Replaced By
type: ObjectProperty
type: AnnotationProperty
type: NamedIndividual
comment: A related resource that supplants, displaces, or supersedes the described resource.
sub property of: Relation
Has Version: isReplacedBy-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

has content
type: ObjectProperty
domain: Physical
domain: Agent
domain: ContentBearingObject
domain: Object
domain: ContentBearingPhysical
domain: Restriction
  all values from: RepresentationForm
  on property: has representation style
domain: Restriction
  cardinality: 1
  on property: has representation style
domain: Restriction
  cardinality: 1
  on property: has content
domain: Restriction
  all values from: Proposition
  on property: has content
range: Abstract
range: Entity

Location, Period, or Jurisdiction
type: NamedIndividual
type: Class
comment: A location, period of time, or jurisdiction.
Has Version: LocationPeriodOrJurisdiction-001
issued: 2008-01-14

ISO 3166
type: NamedIndividual
type: Datatype
comment: The set of codes listed in ISO 3166-1 for the representation of names of countries.
Has Version: ISO3166-004
issued: 2000-07-11
modified: 2008-01-14

Relation
type: AnnotationProperty
comment: A related resource.
description: Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system.
Has Version: relation-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

Record
type: Class
subclass of: Bucket
subclass of: Structured
subclass of: Pattern
subclass of: NonTextual
subclass of: Container
subclass of: Restriction
  has value: false
  on property: canContainHomonymousElements
subclass of: Class
  complement of: Textual
subclass of: Class
  union of: Block
  union of: Container
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
subclass of: Class
  union of: Container
  union of: Popup
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Container
    union of: Field
    union of: Meta
  on property: contains
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: true
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: false
    on property: canContainHomonymousElements
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
subclass of: Restriction
  has value: true
  on property: canContainHeteronymousElements

DDC
type: VocabularyEncodingScheme
type: NamedIndividual
comment: The set of conceptual resources specified by the Dewey Decimal Classification.
Has Version: DDC-003
issued: 2000-07-11
modified: 2008-01-14

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: Section
  on property: contains

Class
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: TableBox
  on property: contains

W3C-DTF
type: NamedIndividual
type: Datatype
comment: The set of dates and times constructed according to the W3C Date and Time Formats Specification.
Has Version: W3CDTF-003
issued: 2000-07-11
modified: 2008-01-14

Class
subclass of: Class
  union of: Block
  union of: Container
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

Class
subclass of: Restriction
  has value: true
  on property: canContainHomonymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: Section
  on property: contains

Subject
type: AnnotationProperty
comment: The topic of the resource.
description: Typically, the subject will be represented using keywords, key phrases, or classification codes. Recommended best practice is to use a controlled vocabulary.
Has Version: subject-007
issued: 1999-07-02
modified: 2012-06-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

Title
type: AnnotationProperty
comment: A name given to the resource.
Has Version: title-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

RepresentationExperimentalObservation
type: Class
subclass of: Object
subclass of: ContentBearingPhysical
subclass of: ContentBearingObject
subclass of: Agent
subclass of: Physical
subclass of: Restriction
  all values from: Proposition
  on property: has content
subclass of: Restriction
  cardinality: 1
  on property: has representation style
subclass of: Restriction
  all values from: RepresentationForm
  on property: has representation style
subclass of: Restriction
  cardinality: 1
  on property: has content

DCMI Metadata Terms - other
type: NamedIndividual
modified: 2012-06-14
publisher: DCMI

ObjectEffect
type: Class
subclass of: Proposition
subclass of: Abstract
subclass of: ExperimentalHypothesis
subclass of: Entity
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has complexity

Class
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

AlternativeHypothesis
type: Class
subclass of: Entity
subclass of: Abstract
subclass of: Proposition
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  cardinality: 1
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity

Bibliographic Citation
type: NamedIndividual
type: AnnotationProperty
type: DatatypeProperty
comment: A bibliographic reference for the resource.
domain: BibliographicResource
range: Literal
sub property of: Identifier
description: Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible.
Has Version: bibliographicCitation-002
issued: 2003-02-15
modified: 2008-01-14

LIMES
type: Framework
type: Approach
type: NamedIndividual
accessed API: SPARQL API
address problem: Filtering
has GUI: true
implemented in: Java
input type: RDF,SPARQL, CSV
provide solution: space tiling

References
type: AnnotationProperty
type: NamedIndividual
type: ObjectProperty
comment: A related resource that is referenced, cited, or otherwise pointed to by the described resource.
sub property of: Relation
Has Version: references-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Class
subclass of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

KnoFuss
type: Framework
type: NamedIndividual
accessed API: SPARQL API
has GUI: false
implemented in: Java
input type: SPARQL/ RDF
provide solution: indexing
vendor: open source

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: FigureBox
  on property: contains

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: TableBox
  on property: contains

Class
subclass of: Pattern
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

SERIMI evalaution
type: Evaluation
type: NamedIndividual
has experiment setup: We have loaded all these datasets into an open-source instance of Virtuoso Universal server10, where around 2GB of data were loaded. An exception was the DBPedia dataset, which we accessed online via its Sparql endpoint. The Virtuoso server was installed in a Mac OS X – version 10.5.8, with 2.4 GHz Intel Core 2 Duo processor and with 4 GB 1067 MHz DDR3 of memory. We ran the script that implements the SERIMI approach directly over the local SPARQL endpoints and DBPedia online endpoint.
uses information asset: Drugbank
uses information asset: DBpedia

Source
type: AnnotationProperty
type: ObjectProperty
type: NamedIndividual
comment: A related resource from which the described resource is derived.
sub property of: Source
sub property of: Relation
description: The described resource may be derived from the related resource in whole or in part. Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system.
Has Version: sourceT-001
issued: 2008-01-14
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Semantic Relation Composition in Large Scale
type: Article
type: Publication
type: RegularPaper
type: NamedIndividual
type: SingleAuthorPublication
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
creator: Kristian Koltho
creator: Arnab Dutta
abstract: Semantic relation composition is a generalized approach for finding conjunctive relation paths in a knowledge base (KB). In semantic web, direct and inverse relationships between entities provide us with ample of explicit knowledge. But there is a plethora of implicit knowledge beyond these direct paths. Consider a knowledge graph, we can achieve deeper insights about a particular entity if we consider the information shared by its neighboring entities via its adjacent relation paths of arbitrary lengths. In this paper, we devise a technique to automatically discover semantically enriched conjunctive relations in a KB.
keywords: Relation composition, Knowledge Bases, Rule mining, Ontologies, Information Extraction

Is Format Of
type: AnnotationProperty
type: ObjectProperty
type: NamedIndividual
comment: A related resource that is substantially the same as the described resource, but in another format.
sub property of: Relation
Has Version: isFormatOf-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Language
type: AnnotationProperty
comment: A language of the resource.
description: Recommended best practice is to use a controlled vocabulary such as RFC 4646 [RFC4646].
Has Version: language-007
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

GroupExperimentalObject
type: Class
subclass of: Role
subclass of: Relation
subclass of: Predicate
subclass of: Entity
subclass of: ActorRole
subclass of: Action-relatedRole
subclass of: Abstract
subclass of: Restriction
  cardinality: 1
  on property: has admin info

Splendid
type: Approach
type: Framework
type: NamedIndividual
address problem: SPARQL query Federation
has GUI: false
provide solution: Hash Join
provide solution: Bind Join
support technology: SPARQL 1.0
platform: Sesame

Format
type: AnnotationProperty
comment: The file format, physical medium, or dimensions of the resource.
description: Examples of dimensions include size and duration. Recommended best practice is to use a controlled vocabulary such as the list of Internet Media Types [MIME].
Has Version: format-007
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

RFC 3066
type: Datatype
type: NamedIndividual
comment: The set of tags constructed according to RFC 3066 for the identification of languages.
description: RFC 3066 has been obsoleted by RFC 4646.
Has Version: RFC3066-002
issued: 2002-07-13
modified: 2008-01-14

has login
type: ObjectProperty
domain: Relation
domain: Action-relatedRole
domain: Entity
domain: SubjectRole
domain: Role
domain: Abstract
domain: Predicate
domain: ActorRole
domain: Restriction
  all values from: LoginName
  on property: has login
domain: Restriction
  all values from: AdminInfoUser
  on property: has admin info
domain: Restriction
  cardinality: 1
  on property: has admin info
domain: Restriction
  all values from: Password
  on property: has pasword
range: Name
range: Attribute
range: Entity
range: Abstract

Class
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Container
    union of: Field
    union of: Meta
  on property: contains
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

Type
type: AnnotationProperty
comment: The nature or genre of the resource.
description: Recommended best practice is to use a controlled vocabulary such as the DCMI Type Vocabulary [DCMITYPE]. To describe the file format, physical medium, or dimensions of the resource, use the Format element.
Has Version: type-006
issued: 1999-07-02
modified: 2008-01-14
note: A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.

ISO 639-2
type: NamedIndividual
type: Datatype
comment: The three-letter alphabetic codes listed in ISO639-2 for the representation of names of languages.
Has Version: ISO639-2-003
issued: 2000-07-11
modified: 2008-01-14

has generality
type: ObjectProperty
domain: Proposition
domain: Abstract
domain: Entity
domain: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
domain: Restriction
  cardinality: 1
  on property: has explicitness
domain: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
domain: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
domain: Restriction
  cardinality: 1
  on property: has generality
domain: Restriction
  all values from: TargetVariable
  on property: has target variable
domain: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
domain: Restriction
  cardinality: 1
  on property: has complexity
range: Entity
range: Abstract
range: Attribute
range: AttributeOfHypothesis

Alternative Title
type: AnnotationProperty
type: NamedIndividual
type: DatatypeProperty
comment: An alternative name for the resource.
range: Literal
sub property of: title
sub property of: Title
description: The distinction between titles and alternative titles is application-specific.
Has Version: alternative-003
issued: 2000-07-11
modified: 2010-10-11

has sampling
type: ObjectProperty
domain: Action-relatedRole
domain: ObjectOfExperiment
domain: Role
domain: Abstract
domain: Entity
domain: ActorRole
domain: Relation
domain: Predicate
domain: Restriction
  cardinality: 1
  on property: has admin info
range: Abstract
range: Procedure
range: ExperimentalMethod
range: Proposition
range: Entity
range: ResearchMethod
range: Restriction
  all values from: MethodGoal
  on property: has goal
range: Class
  union of: ExperimentalMethod
  union of: ProcedureExecuteExperiment
range: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
range: Restriction
  all values from: PlanOfExperimentalActions
  on property: has plan
range: Restriction
  all values from: MethodApplicabilityCondition
  on property: has applicability
range: Restriction
  all values from: SubjectMethod
  on property: has subject
range: Class
  union of: ExperimentalAction
  union of: ExperimentalMethod
  union of: ScientificExperiment

Querying the Web of Data with Graph Theory-based Techniques
type: Publication
type: SingleAuthorPublication
type: Article
type: RegularPaper
type: NamedIndividual
type: Restriction
  all values from: Person
  on property: author
type: Restriction
  all values from: Publication
  on property: cite
has conclusion: In this paper we present a novel graph theory-based approach for improved performance and scalability of distributed SPARQL query processing. We propose several key techniques adopted in the distributed SPARQL query engine that we developed (GDS). First, an extended MST algorithm which supports arbitrary SPARQL queries and provides better performance. Second, a simplied cost model using run-time statistics that lows requirement of service descriptions but provides good cost estimations. Third, a combination of semi-join and bind-join along with local cache that reduce network trac. We also compare our approach with DARQ and FedX in terms of performance and scalability. The results suggest that graph theory-based approach using lightweight service descriptions can provide better performance and scalability over other approaches Although these results are encouraging, the potential of graph theory-based approach can be developed further. First, GDS applies MST-based algorithm to BGPs rather than the whole query. BGPs are optimized separately even they are from the same query (i.e. UNION and OPTIONAL queries). In the future we aim to work on representing the whole query as a single graph and therefore provide better optimization for queries having multiple BGPs. Second, GDS does not take advantage of FILTER in optimization, which would further improve performance. Third, accurate and detailed service descriptions matters. The more accurate statistics we have, the more optimal query plan we get. Currently collecting quality service descriptions are not feasible on a large scale since most SPARQL endpoints do not provide service descriptions. However, this situation is improving as more and more approaches coming up. For example, SPARQL 1.1 aggregation features enable us to collect service descriptions more efficiently, and VoiD encourages SPARQL endpoints to publish service descriptions as well. Fourth, aggregation features in the upcoming SPARQL 1.1 (e.g. BINDINGS) can…
has future work: In addition to these improvements, we are planning to explore the co-reference issue in the Linked Data cloud. The perspective of distributed SPARQL queries, this issue is getting worse as more data are published [3], and we plan to address this issue by using our Virtual Graph approach.
propose framework: GDS
subject: Querying Distributed RDF Data Sources
creator: Hugh C
creator: Xin Wang
creator: Thanassis Tiropanis
abstract: The increasing amount of Linked Data on the Web enables users to retrieve quality and complex information and to deploy innovative, added-value applications. The volume of available Linked Data and their spread across a large number of repositories make a strong case for ecient distributed SPARQL queries. However, in practice, current distributed SPARQL query processing techniques face issues on performance and scalability. In our previous work we provided initial evidence that graph theory-based techniques can address performance issues better than other approaches such as DARQ. Here we further exploit the potential of graph algorithms and we show how they can address performance and scalability for distributed SPARQL queries even better. To that end, we present an improved engine called GDS and we evaluate it by providing a detailed comparison to existing approaches for distributed queries (i.e. DARQ and FedX). By analyzing the evaluation results, we try to identify promising techniques for distributed SPARQL processing, and to outline the problems that need to be addressed in future research.
title: Querying the Web of Data with Graph Theory-based Techniques
year: 2006

Physical Medium
type: Class
type: NamedIndividual
comment: A physical material or carrier.
subclass of: MediaTypeOrExtent
subclass of: MediaType
description: Examples include paper, canvas, or DVD.
Has Version: PhysicalMedium-001
issued: 2008-01-14

SemsurQ2
type: Imp
comment: Count all single author publications in the Ontology
is rule enabled: true
body: ClassAtom
  argument1: http://purl.org/SemSur/SemSur#p
  class predicate: SingleAuthorPublication
head: BuiltinAtom
  arguments: http://purl.org/SemSur/SemSur#p
  builtin: select

has reference
type: ObjectProperty
domain: AdministrativeInformation
domain: Entity
domain: Abstract
domain: Proposition
domain: Class
  union of: AdminInfoExperiment
  union of: StandardOperatingProcedure
domain: Class
  union of: AdminInfoAuthor
  union of: AdminInfoExperiment
  union of: AdminInfoObjectOfExperiment
  union of: AdminInfoProvider
  union of: AdminInfoSubjectOfExperiment
  union of: AdminInfoSubmitter
  union of: AdminInfoUser
domain: Class
  union of: AdminInfoExperiment
  union of: AdminInfoSubjectOfExperiment
  union of: Human
domain: Class
  union of: AdminInfoExperiment
  union of: Human
domain: Restriction
  all values from: TitleExperiment
  on property: has title
domain: Restriction
  all values from: IDExperiment
  on property: has ID
domain: Restriction
  all values from: Author
  on property: has author
domain: Restriction
  all values from: Title
  on property: has title
domain: Class
  union of: AdminInfoExperiment
  union of: ProcedureExecuteExperiment
domain: Restriction
  all values from: StatusExperimentalDocument
  on property: has status
domain: Restriction
  all values from: Organization
  on property: has organization
domain: Restriction
  all values from: BiblioReference
  on property: has reference
domain: Restriction
  cardinality: 1
  on property: has ID
range: Abstract
range: RepresentationForm
range: Predicate
range: Relation
range: Reference
range: Entity
range: Role
range: Restriction
  all values from: Artifact
  on property: has
range: Restriction
  all values from: LinguisticExpression
  on property: has expression
range: Class
  union of: BiblioReference
  union of: ModelRepresentation
  union of: RepresentationExperimentalExecutionProcedure
  union of: RepresentationExperimentalGoal

Multi-factorExperiment
type: Class
subclass of: Classification
subclass of: ScientificTask
subclass of: Proposition
subclass of: Abstract
subclass of: Entity
subclass of: ClassificationOfExperiments
subclass of: Restriction
  all values from: Number
  on property: has number of factors
subclass of: Restriction
  cardinality: 1
  on property: has number of factors
subclass of: Class
  union of: ClassificationOfDomains
  union of: ClassificationOfExperiments

DCMI Type Vocabulary
type: VocabularyEncodingScheme
type: NamedIndividual
comment: The set of classes specified by the DCMI Type Vocabulary, used to categorize the nature or genre of the resource.
Has Version: DCMIType-005
issued: 2000-07-11
modified: 2012-06-14

SERIMI - Resource Description Similarity, RDF Instance Matching and Interlinking.
type: NamedIndividual
type: Article
type: RegularPaper
has conclusion: RDF instance matching in the context of interlinking RDF datasets published in the Linked Data Cloud is the task of determining if two resources are referred to the same entity in the real world. This is a challenging task in high demand by data publishers that wish to interlink their datasets in the cloud. In this work, we propose a novel approach, called SERIMI, for solving the RDF instance-matching problem automatically. SERIMI matches instances between a source and target datasets, without prior knowledge of the data, domain or schema of these datasets. It does so by approximating the notion of similarity by pairing instances based on entity labels as well as structural (ontological) context. As part of the SERIMI approach, we proposed the CRDS function to approximate that judgment of similarity. We used two collections proposed by the OAEI 2010 initiative to evaluate SERIMI. On average, SERIMI outperforms two representative systems, RiMOM and ObjectCoref, which tried to solve the same problem using the same collections and reference alignment, in 70% of the cases.
has future work: As future work, we intend to investigate how our model can be adjusted to consider partial string matching in the similarity function that we proposed, and to accommodate different score distribution metrics as the threshold for the parameter δthreshold. Also, we intend to evaluate this approach in different collections that may provide a more accurate reference alignment than the ones that we used in this work.
propose framework: SERIMI
subject: Link Discovery
creator: Daniel Schwabe
creator: Samur Araujo
creator: Arjen P. de Vries
creator: Jan Hidders
abstract: The interlinking of datasets published in the Linked Data Cloud is a challenging problem and a key factor for the success of the Semantic Web. Manual rule-based methods are the most effective solution for the problem, but they require skilled human data publishers going through a laborious, error prone and time-consuming process for manually describing rules mapping instances between two datasets. Thus, an automatic approach for solving this problem is more than welcome. In this paper, we propose a novel interlinking method, SERIMI, for solving this problem automatically. SERIMI matches instances between a source and a target datasets, without prior knowledge of the data, domain or schema of these datasets. Experiments conducted with benchmark collections demonstrate that our approach considerably outperforms state-of-the-art automatic approaches for solving the interlinking problem on the Linked Data Cloud.
publisher: International Workshop on Ontology Matching
title: SERIMI - Resource Description Similarity, RDF Instance Matching and Interlinking.
year: 2011

Has Part
type: NamedIndividual
type: AnnotationProperty
comment: A related resource that is included either physically or logically in the described resource.
sub property of: Relation
Has Version: hasPart-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Identifier
type: DatatypeProperty
type: NamedIndividual
type: AnnotationProperty
comment: An unambiguous reference to the resource within a given context.
range: Literal
sub property of: Identifier
description: Recommended best practice is to identify the resource by means of a string conforming to a formal identification system.
Has Version: identifierT-001
issued: 2008-01-14
modified: 2008-01-14

a Plug-in for Importing Schemas and Data from Relational Databases into Protégé
type: Article
type: RegularPaper
type: NamedIndividual
has conclusion: In this paper we have presented the DataMaster Protégé plug-in, which allows a user to import schema structures and data from relational databases accessible through JDBC. We have presented the four ontologies that can be used to represent the database structure and the table data and finally we have given a short overview of the different import options available in the DataMaster plug-in.
propose framework: DataMaster
subject: Bringing Relational Databases into the Semantic Web
creator: Martin O’Connor
creator: Samson Tu
creator: Csongor Nyulas
abstract: We present DataMaster, a Protégé plug-in that supports the importing of schema structure and data from relational databases into Protégé. The plug-in supports both OWL and frames-based ontologies and can be used with any relational database with JDBC/ODBC drivers.
publisher: International Protégé Conference
title: DataMaster – a Plug-in for Importing Schemas and Data from Relational Databases into Protégé
year: 2007

Provenance
type: ObjectProperty
type: NamedIndividual
comment: A statement of any changes in ownership and custody of the resource since its creation that are significant for its authenticity, integrity, and interpretation.
range: ProvenanceStatement
description: The statement may include a description of any changes successive custodians made to the resource.
Has Version: provenance-002
issued: 2004-09-20
modified: 2008-01-14

Class
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: ListOfReferences
intersection of: Restriction
  on property: contains
  some values from: BibliographicReference

Class
subclass of: Class
  union of: HeadedContainer
  union of: Table
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements

ListOfAuthors
type: Class
subclass of: List
subclass of: Table
subclass of: Pattern
subclass of: DiscourseElement
subclass of: Structured
subclass of: Container
subclass of: Bucket
subclass of: NonTextual
subclass of: Restriction
  on property: contains
  some values from: Pattern
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Field
    union of: Class
      intersection of: Container
      intersection of: Class
        complement of: Class
  on property: contains
subclass of: Class
  union of: HeadedContainer
  union of: Table
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta
subclass of: Class
  union of: Block
  union of: Container
subclass of: Class
  intersection of: Table
  intersection of: Restriction
    on property: contains
    some values from: Pattern
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
subclass of: Restriction
  has value: true
  on property: canContainHomonymousElements
subclass of: Class
  complement of: Textual
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
subclass of: Class
  union of: Container
  union of: Popup
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Container
    union of: Field
    union of: Meta
  on property: contains

DARQ Evaluation
type: Evaluation
type: DiscourseElement
type: Benchmark
type: NamedIndividual
follows evaluation method: DARQEvalMethod
has experiment setup: Split all data over two Sun-Fire-880 machines (8x sparcv9 CPU, 1050 Mhz, 16GB RAM) running SunOS 5.10. The SPARQL endpoints were provided using Virtuoso Server 5.0.3 with an allowed memory usage of 8 GB . Note that, although we use only two physical servers, there were five logical SPARQL endpoints. DARQ was running on Sun Java 1.6.0 on a Linux system with Intel Core Duo CPUs, 2.13 GHz and 4GB RAM. The machines were connected over a standard 100Mbit network connection.
has result: DARQ results
has input: DBpedia

LCC
type: VocabularyEncodingScheme
type: NamedIndividual
comment: The set of conceptual resources specified by the Library of Congress Classification.
Has Version: LCC-003
issued: 2000-07-11
modified: 2008-01-14

ObservationalError
type: Class
subclass of: Relation
subclass of: Process-relatedRole
subclass of: ProductRole
subclass of: Predicate
subclass of: Entity
subclass of: Role
subclass of: Abstract
subclass of: Restriction
  all values from: Dispersion
  on property: has statistics characteristic
subclass of: Restriction
  cardinality: 1
  on property: has statistics characteristic
subclass of: Restriction
  all values from: LevelOfSignificance
  on property: has statistics characteristic
subclass of: Class
  union of: ObservationalError
  union of: ResultError

has procedure
type: ObjectProperty
domain: Entity
domain: Procedure
domain: Proposition
domain: Abstract
domain: ProcedureExecuteExperiment
domain: Restriction
  all values from: TimePoint
  on property: has latest modification
domain: Class
  union of: AdminInfoExperiment
  union of: ProcedureExecuteExperiment
domain: Class
  union of: ExperimentalGoal
  union of: Model
  union of: ProcedureExecuteExperiment
domain: Restriction
  cardinality: 1
  on property: has latest modification
domain: Restriction
  cardinality: 1
  on property: has status
domain: Restriction
  all values from: PermissionStatus
  on property: has status
domain: Class
  union of: AdminInfoExperiment
  union of: StandardOperatingProcedure
domain: Restriction
  all values from: StatusExperimentalDocument
  on property: has status
domain: Restriction
  all values from: PlanOfExperimentalActions
  on property: has plan
domain: Class
  union of: ExperimentalMethod
  union of: ProcedureExecuteExperiment
domain: Restriction
  all values from: ProcedureExecuteExperiment
  on property: has procedure
domain: Restriction
  all values from: BiblioReference
  on property: has representation
domain: Restriction
  all values from: RepresentationExperimentalExecutionProcedure
  on property: has representation
domain: Restriction
  all values from: Author
  on property: has author
domain: Restriction
  all values from: DocumentStage
  on property: has status
domain: Restriction
  all values from: AuthorSOP
  on property: has author
range: Proposition
range: Procedure
range: Entity
range: Abstract
range: Class
  union of: ExperimentalMethod
  union of: ProcedureExecuteExperiment
range: Restriction
  all values from: PlanOfExperimentalActions
  on property: has plan
range: Restriction
  cardinality: 1
  on property: has status
range: Restriction
  all values from: RepresentationExperimentalExecutionProcedure
  on property: has representation
range: Class
  union of: ExperimentalGoal
  union of: Model
  union of: ProcedureExecuteExperiment
range: Restriction
  all values from: PermissionStatus
  on property: has status
range: Restriction
  all values from: StatusExperimentalDocument
  on property: has status
range: Restriction
  all values from: DocumentStage
  on property: has status
range: Class
  union of: AdminInfoExperiment
  union of: ProcedureExecuteExperiment

has sample size
type: ObjectProperty
domain: Relation
domain: Role
domain: ActorRole
domain: ObjectOfExperiment
domain: Predicate
domain: Abstract
domain: Action-relatedRole
domain: Entity
domain: Restriction
  cardinality: 1
  on property: has admin info
range: AttributeGroup
range: Entity
range: Attribute
range: Abstract
range: AttributeSample

Class
subclass of: Pattern
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

TestingEffect
type: Class
subclass of: Proposition
subclass of: AlternativeHypothesis
subclass of: Abstract
subclass of: Entity
subclass of: ExperimentalHypothesis
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  cardinality: 1
  on property: has complexity
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality

Class
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: List
intersection of: Restriction
  all values from: Reference
  on property: contains

SemWIQ
type: ProblemSolution
type: Approach
type: NamedIndividual
type: Framework
type: Problem
address problem: Query Execution
address problem: SPARQL query Federation
address problem: Source Selection
has GUI: true
has solution: Statistic of Predicate
implemented in: Java
provide solution: Bind Join
support technology: SPARQL 1.1
platform: Jena

ISO 639-3
type: NamedIndividual
type: Datatype
comment: The set of three-letter codes listed in ISO 639-3 for the representation of names of languages.
Has Version: ISO639-3-001
issued: 2008-01-14

SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions
type: NamedIndividual
type: Article
type: RegularPaper
has conclusion: SPLENDID allows for transparent query federation over distributed SPARQL endpoints. In order to achieve a good query execution performance, data source selection and query optimization is based on basic statistical information which is obtained from VOID descriptions. The utilization of open semantic web standards, like VOID and SPARQL endpoints, allows for flexible integration of various distributed and linked RDF data sources. We have described in detail the implementation of the data source selection and the join order optimization. The evaluation shows that our approach can achieve good query performance and is competitive compared to other state-of-the-art federation implementations. In our analysis of the source selection we came to the conclusion that at least predicate and type statistics should be included in VOID description for RDF datasets. The use of 3rd party sameAs links, however, can significantly increase the number of requests and thus, hamper the efficiency of query execution plans. The comparison of the two employed physical join implementations has shown that the network overhead plays an important role. Both hash join and bind join can significantly reduce the query processing time for certain types of queries. With SPLENDID we also like to advocate the adoption of VOID statistics for Linked Data. As next steps, we plan to investigate whether VOID descriptions can easily be extended with more detailed statistics in order to allow for more accurate cardinality estimates and, thus, better query execution plans. On the other hand, the actual query execution has not yet been optimized in SPLENDID. Therefore, we plan to integrate optimization techniques as used in FedX. Moreover, the adoption of the SPARQL 1.1 federation extension will also allow for more efficient query execution.
has future work: As next steps, we plan to investigate whether VOID descriptions can easily be extended with more detailed statistics in order to allow for more accurate cardinality estimates and, thus, better query execution plans. On the other hand, the actual query execution has not yet been optimized in SPLENDID. Therefore, we plan to integrate optimization techniques as used in FedX. Moreover, the adoption of the SPARQL 1.1 federation extension will also allow for more efficient query execution.
propose framework: Splendid
subject: Querying Distributed RDF Data Sources
creator: Steffen Staab
creator: Olaf Gorlitz
abstract: In order to leverage the full potential of the Semantic Web it is necessary to transparently query distributed RDF data sources in the same way as it has been possible with federated databases for ages. However, there are significant differences between the Web of (linked) Data and the traditional database approaches. Hence, it is not straightforward to adapt successful database techniques for RDF federation. Reasons are the missing cooperation between SPARQL endpoints and the need for detailed data statistics for estimating the costs of query execution plans. We have implemented SPLENDID, a query optimization strategy for federating SPARQL endpoints based on statistical data obtained from voiD descriptions.
publisher: International Conference on Consuming Linked Data
title: SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions
year: 2011

MaturationEffect
type: Class
subclass of: Entity
subclass of: Proposition
subclass of: Abstract
subclass of: AlternativeHypothesis
subclass of: ExperimentalHypothesis
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  cardinality: 1
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has generality

Table Of Contents
type: NamedIndividual
type: AnnotationProperty
type: DatatypeProperty
comment: A list of subunits of the resource.
sub property of: Description
sub property of: description
Has Version: tableOfContents-003
issued: 2000-07-11
modified: 2008-01-14

Audience Education Level
type: NamedIndividual
type: ObjectProperty
comment: A class of entity, defined in terms of progression through an educational or training context, for which the described resource is intended.
range: Class
range: AgentClass
sub property of: audience
Has Version: educationLevel-002
issued: 2002-07-13
modified: 2008-01-14

Relation
type: NamedIndividual
type: AnnotationProperty
comment: A related resource.
sub property of: Relation
description: Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system.
Has Version: relationT-001
issued: 2008-01-14
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

has hypothesis representation
type: ObjectProperty
domain: Entity
domain: Proposition
domain: Abstract
domain: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
domain: Restriction
  all values from: TargetVariable
  on property: has target variable
domain: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
domain: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
domain: Restriction
  cardinality: 1
  on property: has complexity
domain: Restriction
  cardinality: 1
  on property: has generality
domain: Restriction
  cardinality: 1
  on property: has explicitness
domain: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
range: Representation
range: Physical
range: Agent
range: Object
range: ContentBearingObject
range: ContentBearingPhysical
range: Restriction
  all values from: Proposition
  on property: has content
range: Restriction
  cardinality: 1
  on property: has content
range: Restriction
  cardinality: 1
  on property: has representation style
range: Restriction
  all values from: RepresentationForm
  on property: has representation style

Mediator
type: ObjectProperty
type: NamedIndividual
comment: An entity that mediates access to the resource and for whom the resource is intended or useful.
range: Class
range: AgentClass
sub property of: audience
description: In an educational context, a mediator might be a parent, teacher, teaching assistant, or care-giver.
Has Version: mediator-003
issued: 2001-05-21
modified: 2008-01-14

has explicitness
type: ObjectProperty
domain: Entity
domain: Proposition
domain: Abstract
domain: Restriction
  cardinality: 1
  on property: has explicitness
domain: Restriction
  cardinality: 1
  on property: has generality
domain: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
domain: Restriction
  all values from: TargetVariable
  on property: has target variable
domain: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
domain: Restriction
  cardinality: 1
  on property: has complexity
domain: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
domain: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
range: Entity
range: Attribute
range: AttributeOfHypothesis
range: Abstract

Is Required By
type: NamedIndividual
type: AnnotationProperty
type: ObjectProperty
comment: A related resource that requires the described resource to support its function, delivery, or coherence.
sub property of: Relation
Has Version: isRequiredBy-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Table
type: Class
subclass of: NonTextual
subclass of: Container
subclass of: Bucket
subclass of: Structured
subclass of: Pattern
subclass of: Class
  union of: HeadedContainer
  union of: Table
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
subclass of: Class
  complement of: Textual
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Container
    union of: Field
    union of: Meta
  on property: contains
subclass of: Class
  union of: Container
  union of: Popup
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
subclass of: Class
  union of: Block
  union of: Container
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
subclass of: Restriction
  has value: true
  on property: canContainHomonymousElements
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta

ListOfAgents
type: Class
subclass of: Container
subclass of: Table
subclass of: Bucket
subclass of: DiscourseElement
subclass of: Pattern
subclass of: Structured
subclass of: NonTextual
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta
subclass of: Class
  union of: Block
  union of: Container
subclass of: Class
  intersection of: Table
  intersection of: Restriction
    on property: contains
    some values from: Pattern
subclass of: Class
  union of: HeadedContainer
  union of: Table
subclass of: Class
  intersection of: Bucket
  intersection of: Restriction
    all values from: Class
      union of: Container
      union of: Popup
    on property: isContainedBy
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Container
    union of: Field
    union of: Meta
  on property: contains
subclass of: Class
  union of: Container
  union of: Popup
subclass of: Restriction
  has value: true
  on property: canContainHomonymousElements
subclass of: Restriction
  all values from: Class
    union of: Container
    union of: Popup
  on property: isContainedBy
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
subclass of: Restriction
  all values from: Class
    union of: Block
    union of: Field
    union of: Class
      intersection of: Container
      intersection of: Class
        complement of: Class
  on property: contains
subclass of: Class
  complement of: Textual
subclass of: Restriction
  on property: contains
  some values from: Pattern

has description
type: ObjectProperty
domain: Abstract
domain: Relation
domain: Entity
domain: Role
domain: Predicate
domain: Process-relatedRole
domain: Restriction
  all values from: TechnicalDescription
  on property: has description
domain: Class
  union of: AdminInfoSubjectOfExperiment
  union of: ExperimentalEquipment
  union of: Human
  union of: Organization
domain: Restriction
  all values from: Name
  on property: has name
domain: Restriction
  all values from: NameEquipment
  on property: has name
range: ContentBearingObject
range: Physical
range: Agent
range: ContentBearingPhysical
range: Object
range: Representation
range: Restriction
  cardinality: 1
  on property: has representation style
range: Restriction
  cardinality: 1
  on property: has content
range: Restriction
  all values from: RepresentationForm
  on property: has representation style
range: Restriction
  all values from: Proposition
  on property: has content

Rights Holder
type: ObjectProperty
type: NamedIndividual
comment: A person or organization owning or managing rights over the resource.
range: Agent
Has Version: rightsHolder-002
issued: 2004-06-14
modified: 2008-01-14

Class
subclass of: Class
  intersection of: Container
  intersection of: Restriction
    has value: false
    on property: canContainHeteronymousElements
  intersection of: Restriction
    has value: true
    on property: canContainHomonymousElements
intersection of: Table
intersection of: Restriction
  on property: contains
  some values from: Pattern

Class
subclass of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: ListOfReferences
intersection of: Restriction
  all values from: Restriction
    on property: Relation
    some values from: Section
  on property: contains

Class
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta
intersection of: Container
intersection of: Restriction
  has value: true
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: false
  on property: canContainHomonymousElements

ExperimenterBias
type: Class
subclass of: AlternativeHypothesis
subclass of: Abstract
subclass of: Proposition
subclass of: Entity
subclass of: ExperimentalHypothesis
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  cardinality: 1
  on property: has explicitness
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
subclass of: Restriction
  cardinality: 1
  on property: has complexity

Size or Duration
type: NamedIndividual
type: Class
comment: A dimension or extent, or a time taken to play or execute.
subclass of: MediaTypeOrExtent
description: Examples include a number of pages, a specification of length, width, and breadth, or a period in hours, minutes, and seconds.
Has Version: SizeOrDuration-001
issued: 2008-01-14

has representation style
type: ObjectProperty
domain: ContentBearingObject
domain: Agent
domain: ContentBearingPhysical
domain: Physical
domain: Object
domain: Restriction
  all values from: RepresentationForm
  on property: has representation style
domain: Restriction
  all values from: Proposition
  on property: has content
domain: Restriction
  cardinality: 1
  on property: has representation style
domain: Restriction
  cardinality: 1
  on property: has content
range: Role
range: Entity
range: Predicate
range: Abstract
range: Relation
range: Restriction
  all values from: LinguisticExpression
  on property: has expression
range: Restriction
  all values from: Artifact
  on property: has

Type-Constrained Representation Learning in Knowledge Graphs
type: Article
type: RegularPaper
type: Publication
type: SingleAuthorPublication
type: NamedIndividual
type: Restriction
  all values from: Publication
  on property: cite
type: Restriction
  all values from: Person
  on property: author
creator: Stephan Baier
creator: Volker Tresp
creator: Denis Krompaß
abstract: Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. La- tent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to un- derstand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link- prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type- constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data.

Jurisdiction
type: NamedIndividual
type: Class
comment: The extent or range of judicial, law enforcement, or other authority.
subclass of: Location, Period, or Jurisdiction
Has Version: Jurisdiction-001
issued: 2008-01-14

ExperimentalDesignEffect
type: Class
subclass of: ExperimentalHypothesis
subclass of: Entity
subclass of: Proposition
subclass of: Abstract
subclass of: Restriction
  cardinality: 1
  on property: has generality
subclass of: Restriction
  all values from: TargetVariable
  on property: has target variable
subclass of: Restriction
  all values from: HypothesisComplexity
  on property: has complexity
subclass of: Restriction
  all values from: HypothesisExplicitness
  on property: has explicitness
subclass of: Restriction
  all values from: GeneralityOfHypothesis
  on property: has generality
subclass of: Restriction
  all values from: HypothesisRepresentation
  on property: has hypothesis representation
subclass of: Restriction
  cardinality: 1
  on property: has complexity
subclass of: Restriction
  cardinality: 1
  on property: has explicitness

Has Version
type: ObjectProperty
type: AnnotationProperty
type: NamedIndividual
comment: A related resource that is a version, edition, or adaptation of the described resource.
sub property of: Relation
Has Version: hasVersion-003
issued: 2000-07-11
modified: 2008-01-14
note: This term is intended to be used with non-literal values as defined in the DCMI Abstract Model (http://dublincore.org/documents/abstract-model/). As of December 2007, the DCMI Usage Board is seeking a way to express this intention with a formal range declaration.

Class
subclass of: Class
  union of: Block
  union of: Container
  union of: Field
  union of: Meta
intersection of: Container
intersection of: Restriction
  has value: false
  on property: canContainHeteronymousElements
intersection of: Restriction
  has value: true
  on property: canContainHomonymousElements