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results
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Ontology that defines the conceptual model for the Pilot 5 - Smart Building use case @en
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The module Location models information related to the localization and georeferencing of a cultural property. In this module are used as template the following Ontology Design Patterns: - http://www.ontologydesignpatterns.org/cp/owl/collectionentity.owl - http://www.ontologydesignpatterns.org/cp/owl/classification.owl - http://www.ontologydesignpatterns.org/cp/owl/place.owl - http://www.ontologydesignpatterns.org/cp/owl/timeindexedsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en
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The Catalogue module allows the description of concepts related to the Italian General Catalogue of Cultural Heritage (ICCD-MiBAC), and in particular catalogue records, that is XML files recording all data gathered by a cataloguer on a particular cultural property. @en
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The ontology, presented here in a beta version, is based on the analysis of the documentation and descriptive requirements of the Intesa Sanpaolo Historical Archive and is intended to describe the content of historical banking documents and of some of the activities carried out by the bank, particularly in relation to third parties (loans, charity donations, seizures and confiscations, etc.), which involve the initiation of processes or the production of documents. The focal point of the descriptive model is the bank - an entity that initiates different types of processes, whose common feature is that they are structured into various stages/events - and the relationship between the documentation produced and the information it contains. In fact, this ontology is based on information collected from archived documents which describe various processes and activities carried out by banking institutions: the starting point for its construction were the inventories and databases of documentation stored in the Historical Archive which was produced by the various banks that over time were merged into Intesa Sanpaolo. The ontology was created to provide a sufficiently abstract representation and model for describing the processes of various banking activities from which the documentation was produced - from a company's request for financing and its outcome, to the preparation of seizure, confiscation and asset restitution filings, to charitable contributions, just to mention a few examples - reusing models that were already well established and widely used. The structure of the proposed ontology is in fact intended to adapt to the various activities, described in the archive files that a banking institution performs in relation to third parties. The proposed ontology is therefore not an ontology on banking activity in general, but on the relationship between this activity and the documents that are produced. Moreover, its objective is not to describe the documents in the strict sense of the term, for which reference is made to OAD ontology. The purpose of this project is to lay the initial, and fundamental, building blocks for describing the complexity, variety, and breadth of the domain of archiving bank records and the data they contain. Despite having data from different banks relating to different activities and having already made arrangements for the integration of third-party datasets and ontologies, before completing the project we will have to wait for the processing of representations based on other types of documents and banking institutions, including non-Italian ones. @en
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An ontology to model accountability of generic systems. @en
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The REACT ontology aims to represent all the necessary knowledge to support the achievement of island energy independence through renewable energy generation and storage, a demand response platform, and promoting user engagement in a local energy community. The REACT ontology has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. @en
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The SEAS Device ontology defines `seas:Device` as physical system that are designed to execute one or more procedures that involve the physical world. @en
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Ontology defining concepts for Entity Legal Forms and their abbreviations by jurisdiction, based on ISO 20275. Though used by Global Legal Entity Identifier Foundation (GLEIF) for Legal Entity Identifier registration, it is more broadly applicable. @en
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An ontology for using languages as resources @en
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A vocabulary defining annotations enabling loose coupling between classes and properties in ontologies. Those annotations define with some accuracy the expected use of properties, in particular across vocabularies, without the formal constraints entailed by the use of OWL or RDFS constructions @en
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Ontology for surveys based on the Coney data model. @en
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The General Ontology for Linguistic Description (GOLD) was created primarily for applications involving descriptive linguistics. @en
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SemTS is an ontology designed to identify and describe segments within time series data, which are specific data points or intervals that can overlap. These segments encompass characteristic knowledge about the time interval they cover, including common time series features, structural anomalies, motifs, or information provided by domain experts. By classifying and semantically representing this knowledge, SemTS promotes organized reusability and efficient propagation, potentially reducing resource expenditure while enhancing future analyses. It employs established semantic approaches. Examples are DCAT to reference associated time series data, OWL-Time to define the index structure of time series data and segments or ML-Schema to expand the expressiveness regarding data analysis task information. SemTS's design involves categorizing time series knowledge and mapping it to specific intervals and dimensions of time series data. It introduces a class called TimeSeriesSegment to model these segments, extending the DCAT Dataset class to enable segments to be part of other segments. This structure allows for the association of knowledge, such as anomalies, with particular intervals or data points. TimeIndex specifications extend OWL-Time classes, while dimensional details are represented by DataDimension. The segment-wise consideration of knowledge indirectly serves as an index structure, linking meaningful time series data with categorized knowledge. At the highest level of abstraction, time series knowledge is divided into three categories: DataKnowledge, ScenarioKnowledge, and MethodKnowledge. DataKnowledge refers to insights extracted directly from the data or through analytical methods, such as class membership from time series clustering. ScenarioKnowledge describes verified contexts, including data annotations or domain-specific process knowledge, often equating to expert-provided a priori information and can also define facts derived from inferred knowledge. MethodKnowledge encompasses effective analytical method presets or mathematical/logical equivalents of established process information. @en
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The AtomOWL ontology is inspired from the work done by the atom working group. This ontology is working off the rfc 4287 published among othe places at http://www.atompub.org/rfc4287.html . The AtomOWL ontology uses as much as possible the same terms as the format there to make the relation easy to understand. The AtomOWL name space is slightly different from the atom namespace [see post http://www.imc.org/atom-syntax/mail-archive/msg16476.html]. But this is a good thing as it helps distinguish the ontology from the rfc 4287 serialisation. @en
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The DBpedia ontology provides the classes and properties used in the DBpedia data set. @en