The Experts below are selected from a list of 38589 Experts worldwide ranked by ideXlab platform
Christopher G Chute - One of the best experts on this subject based on the ideXlab platform.
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cntro 2 0 a harmonized Semantic Web Ontology for temporal relation inferencing in clinical narratives
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2011Co-Authors: Cui Tao, Harold R Solbrig, Christopher G ChuteAbstract:The Clinical Narrative Temporal Relation Ontology (CNTRO) has been developed for the purpose of allowing temporal information of clinical data to be Semantically annotated and queried, and using inference to expose new temporal features and relations based on the Semantic assertions and definitions of the temporal aspects in the Ontology. While CNTRO provides a formal Semantic foundation to leverage the Semantic-Web techniques, it is still necessary to arrive at a shared set of Semantics and operational rules with commonly used ontologies for the time domain. This paper introduces CNTRO 2.0, which tries to harmonize CNTRO 1.0 and a list of existing time ontologies or top-level ontologies into a unified model—an OWL based Ontology of temporal relations for clinical research.
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cntro a Semantic Web Ontology for temporal relation inferencing in clinical narratives
American Medical Informatics Association Annual Symposium, 2010Co-Authors: Cui Tao, Weiqi Wei, Harold R Solbrig, Guergana Savova, Christopher G ChuteAbstract:Using Semantic-Web specifications to represent temporal information in clinical narratives is an important step for temporal reasoning and answering time-oriented queries. Existing temporal models are either not compatible with the powerful reasoning tools developed for the Semantic Web, or designed only for structured clinical data and therefore are not ready to be applied on natural-language-based clinical narrative reports directly. We have developed a Semantic-Web Ontology which is called Clinical Narrative Temporal Relation Ontology. Using this Ontology, temporal information in clinical narratives can be represented as RDF (Resource Description Framework) triples. More temporal information and relations can then be inferred by Semantic-Web based reasoning tools. Experimental results show that this Ontology can represent temporal information in real clinical narratives successfully.
Claus Pahl - One of the best experts on this subject based on the ideXlab platform.
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adaptive e learning content generation based on Semantic Web technology
Artificial Intelligence in Education, 2005Co-Authors: Edmond Holohan, Mark Melia, Declan Mcmullen, Claus PahlAbstract:The efficient authoring of learning content is a central problem of courseware engineering. Courseware authors will appreciate the benefits of tools which automate various authoring tasks. We describe a system, OntAWare, which provides an environment comprising a set of software tools that support learning content authoring, management and delivery. This system exploits an opportunity provided by the emerging technologies of the Semantic Web movement, most notably knowledge-representation standards and knowledge-processing techniques. The system represents a combination of these newer developments with earlier work in areas such as artificial intelligence (AI) and intelligent tutoring systems (ITS). A key feature of the authoring environment is the semi-automatic generation of standard e-learning and other courseware elements (learning objects). Widely available standardised knowledge representations (ontologies) and Ontology-structured content are used as source material. Standard courseware elements are produced by the application of graph transformations to these ontologies. The resulting products can be hosted by standards-compliant delivery environments. Adaptivity is an important characteristic of the system as a whole. Authors can select and customise new or existing subject ontologies and employ an appropriate teaching/learning strategy in the generation of learning objects. Instructors can configure the delivery environment either to offer strictly sequenced presentations to students, or to allow also varying degrees of free student navigation, based on the the runtime incorporation of domain ontologies. Students in turn can take the generated courses in the preconfigured delivery environment, and this delivery is dynamically customised to the individual student's preferences and constantly monitored learning track. The combination of the semi-automatic generation of learning objects with an adaptive delivery environment is a central feature of this new system. Keywords: courseware generation, Semantic Web, Ontology, learning object, adaptivity.
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an Ontology for software component matching
Fundamental Approaches to Software Engineering, 2003Co-Authors: Claus PahlAbstract:The Web is likely to be a central platform for software development in the future. We investigate how Semantic Web technologies, in particular ontologies, can be utilised to support software component development in a Web environment. We use description logics, which underlie Semantic Web Ontology languages such as DAML+OIL, to develop an Ontology for matching requested and provided components. A link between modal logic and description logics will prove invaluable for the provision of reasoning support for component and service behaviour.
Cui Tao - One of the best experts on this subject based on the ideXlab platform.
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cntro 2 0 a harmonized Semantic Web Ontology for temporal relation inferencing in clinical narratives
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2011Co-Authors: Cui Tao, Harold R Solbrig, Christopher G ChuteAbstract:The Clinical Narrative Temporal Relation Ontology (CNTRO) has been developed for the purpose of allowing temporal information of clinical data to be Semantically annotated and queried, and using inference to expose new temporal features and relations based on the Semantic assertions and definitions of the temporal aspects in the Ontology. While CNTRO provides a formal Semantic foundation to leverage the Semantic-Web techniques, it is still necessary to arrive at a shared set of Semantics and operational rules with commonly used ontologies for the time domain. This paper introduces CNTRO 2.0, which tries to harmonize CNTRO 1.0 and a list of existing time ontologies or top-level ontologies into a unified model—an OWL based Ontology of temporal relations for clinical research.
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cntro a Semantic Web Ontology for temporal relation inferencing in clinical narratives
American Medical Informatics Association Annual Symposium, 2010Co-Authors: Cui Tao, Weiqi Wei, Harold R Solbrig, Guergana Savova, Christopher G ChuteAbstract:Using Semantic-Web specifications to represent temporal information in clinical narratives is an important step for temporal reasoning and answering time-oriented queries. Existing temporal models are either not compatible with the powerful reasoning tools developed for the Semantic Web, or designed only for structured clinical data and therefore are not ready to be applied on natural-language-based clinical narrative reports directly. We have developed a Semantic-Web Ontology which is called Clinical Narrative Temporal Relation Ontology. Using this Ontology, temporal information in clinical narratives can be represented as RDF (Resource Description Framework) triples. More temporal information and relations can then be inferred by Semantic-Web based reasoning tools. Experimental results show that this Ontology can represent temporal information in real clinical narratives successfully.
Frank Van Harmelen - One of the best experts on this subject based on the ideXlab platform.
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introduction to Semantic Web Ontology languages
Lecture Notes in Computer Science, 2005Co-Authors: Grigoris Antoniou, Enrico Franconi, Frank Van HarmelenAbstract:The aim of this chapter is to give a general introduction to some of the Ontology languages that play a prominent role on the Semantic Web, and to discuss the formal foundations of these languages. Web Ontology languages will be the main carriers of the information that we will want to share and integrate.
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towards the Semantic Web Ontology driven knowledge management
2002Co-Authors: John Davies, Dieter Fensel, Frank Van HarmelenAbstract:From the Publisher: "Towards the Semantic Web focuses on the application of Semantic Web technology and ontologies in particular to electronically available information to improve the quality of knowledge management in large and distributed organizations. Covering the key technologies for the next generation of the WWW, this book is a mixture of theory, tools and applications in an important area of WWW research." Aimed primarily at researchers and developers in the area of WWW-based knowledge management and information retrieval. It will also be a useful reference for students in computer science at the postgraduate level, academic and industrial researchers in the field, business managers who are aiming to increase the corporations' information infrastructure and industrial personnel who are tracking WWW technology developments in order to understand the business implications.
Harold R Solbrig - One of the best experts on this subject based on the ideXlab platform.
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cntro 2 0 a harmonized Semantic Web Ontology for temporal relation inferencing in clinical narratives
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2011Co-Authors: Cui Tao, Harold R Solbrig, Christopher G ChuteAbstract:The Clinical Narrative Temporal Relation Ontology (CNTRO) has been developed for the purpose of allowing temporal information of clinical data to be Semantically annotated and queried, and using inference to expose new temporal features and relations based on the Semantic assertions and definitions of the temporal aspects in the Ontology. While CNTRO provides a formal Semantic foundation to leverage the Semantic-Web techniques, it is still necessary to arrive at a shared set of Semantics and operational rules with commonly used ontologies for the time domain. This paper introduces CNTRO 2.0, which tries to harmonize CNTRO 1.0 and a list of existing time ontologies or top-level ontologies into a unified model—an OWL based Ontology of temporal relations for clinical research.
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cntro a Semantic Web Ontology for temporal relation inferencing in clinical narratives
American Medical Informatics Association Annual Symposium, 2010Co-Authors: Cui Tao, Weiqi Wei, Harold R Solbrig, Guergana Savova, Christopher G ChuteAbstract:Using Semantic-Web specifications to represent temporal information in clinical narratives is an important step for temporal reasoning and answering time-oriented queries. Existing temporal models are either not compatible with the powerful reasoning tools developed for the Semantic Web, or designed only for structured clinical data and therefore are not ready to be applied on natural-language-based clinical narrative reports directly. We have developed a Semantic-Web Ontology which is called Clinical Narrative Temporal Relation Ontology. Using this Ontology, temporal information in clinical narratives can be represented as RDF (Resource Description Framework) triples. More temporal information and relations can then be inferred by Semantic-Web based reasoning tools. Experimental results show that this Ontology can represent temporal information in real clinical narratives successfully.
