14
results
-
With the aim of enhancing natural communication between workers in industrial environments and the systems to be used by them, TODO (Task-Oriented Dialogue management Ontology) has been developed to be the core of task-oriented dialogue systems. TODO is a core ontology that provides task-oriented dialogue systems with the necessary means to be capable of naturally interacting with workers (both at understanding and at ommunication levels) and that can be easily adapted to different industrial scenarios, reducing adaptation time and costs. Moreover, it allows to store and reproduce the dialogue process to be able to learn from new interactions. @en
-
Module for dialogue process and system output management. @en
-
Module for domain modelling. @en
-
Module for scenario modelling (world elements) of domain. @en
-
Module for Action (and Frame) modelling inside domain. @en
-
Module for dialogue tracing. @en
-
Module for dialogue process and system output management. @en
-
This is the provisional registration of the RDA Group 1 Element Vocabulary, managed by the DCMI/RDA Task Group. @en
-
This vocabulary defines the common characteristics of task management tools or issue tracking systems, such as Jira, Redmine or Trac. @en
-
An expression in RDF of the application profile for collection-level description developed by the Dublin Core Collection Description Task Group. @en
-
The NEPOMUK Calendaring Ontology intends to provide vocabulary for describing calendaring data (events, tasks, journal entries) which is an important part of the body of information usually stored on a desktop. It is an adaptation of the ICALTZD ontology created by the W3C RDF Calendar Task Force of the Semantic Web Interest Group in the Semantic Web Activity. @en
-
This ontology is partially based on the SysML QUDV (Quantities, Units, Dimensions and Values) proposed by a working group of the SysML 1.2 Revision Task Force (RTF), working in close coordination with the OMG MARTE specification group. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, the QU ontology has been further developed as a complement to the Agriculture Meteorology example showcasing the ontology developed by the W3C Semantic Sensor Networks incubator group (SSN-XG). @en
-
The Crime Event Model is an ontology for the representation of crime events extracted from local newspapers. It could be employed for Crime Analysis purposes: extracting crime information from newspapers and enriching them with proper machine-readable semantics is a critical task to help law enforcement agencies at preventing crime, supporting criminal investigations and evaluating the action of law enforcement agencies themselves. The model is based on the fundamental 5W1H journalistic questions, that are Who?, What?, When?, Where?, Why? and How?. Another important requirement was the attempt to exploit existing knowledge graphs and ontologies such as the Simple Event Model (SEM) Ontology and the Schema.org data model for interoperability and interconnection. @en
-
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
1