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Sinopsis
As an essential dimension of our information space, time plays a very important role in every aspect of our lives. A specification of temporal information is necessarily required for a large group of applications, including the Semantic Web and natural language. In response to this need, we have developed a rich ontology of temporal concepts, OWL-Time (formerly DAML-Time), for describing the temporal content of Web pages and the temporal properties of Web services. Since most of the information on the Web is in natural language, it can also be used to increase temporal awareness for natural language applications. The ontology is represented in first-order logic and the OWL Web Ontology Language. It covers a very rich set of temporal concepts, extending Hobbs (2002)¿s work with more complex temporal phenomena, such as temporal aggregates, temporal arithmetic mixing months and days, and vague event durations. Since missing explicit and exact durations is one of the most common sources of incomplete information for temporal reasoning, we have constructed an annotated corpus and applied machine learning techniques to automatically extract implicit and vague event durations from text.
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Reseña del editor
As an essential dimension of our information space, time plays a very important role in every aspect of our lives. A specification of temporal information is necessarily required for a large group of applications, including the Semantic Web and natural language. In response to this need, we have developed a rich ontology of temporal concepts, OWL-Time (formerly DAML-Time), for describing the temporal content of Web pages and the temporal properties of Web services. Since most of the information on the Web is in natural language, it can also be used to increase temporal awareness for natural language applications. The ontology is represented in first-order logic and the OWL Web Ontology Language. It covers a very rich set of temporal concepts, extending Hobbs (2002)¿s work with more complex temporal phenomena, such as temporal aggregates, temporal arithmetic mixing months and days, and vague event durations. Since missing explicit and exact durations is one of the most common sources of incomplete information for temporal reasoning, we have constructed an annotated corpus and applied machine learning techniques to automatically extract implicit and vague event durations from text.
Biografía del autor
Feng Pan is a research scientist at Powerset (acquired by Microsoft in 2008) building a next-generation natural language search engine. He received his Ph.D. in computer science from the University of Southern California in 2007, and conducted research in knowledge representation, NLP, and semantic web at USC Information Sciences Institute (ISI).
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An Ontology of Time: Representing Complex Temporal Phenomena for the Semantic Web and Natural Language
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An Ontology of Time: Representing Complex Temporal Phenomena for the Semantic Web and Natural Language
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An Ontology of Time
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Pan FengFeng Pan is a research scientist at Powerset (acquired bynMicrosoft in 2008) building a next-generation natural languagensearch engine. He received his Ph.D. in computer science from thenUniversity of Southern California in 2. Nº de ref. del artículo: 4959954
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An Ontology of Time : Representing Complex Temporal Phenomena for the Semantic Web and Natural Language
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VDM Verlag Dr. Müller, 2009
ISBN 10: 3639127781
ISBN 13: 9783639127782
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As an essential dimension of our information space,time plays a very important role in every aspect ofour lives. A specification of temporal information isnecessarily required for a large group ofapplications, including the Semantic Web and naturallanguage. In response to this need, we have developeda rich ontology of temporal concepts, OWL-Time(formerly DAML-Time), for describing the temporalcontent of Web pages and the temporal properties ofWeb services. Since most of the information on theWeb is in natural language, it can also be used toincrease temporal awareness for natural languageapplications. The ontology is represented infirst-order logic and the OWL Web Ontology Language.It covers a very rich set of temporal concepts,extending Hobbs (2002) s work with more complextemporal phenomena, such as temporal aggregates,temporal arithmetic mixing months and days, and vagueevent durations. Since missing explicit and exactdurations is one of the most common sources ofincomplete information for temporal reasoning, wehave constructed an annotated corpus and appliedmachine learning techniques to automatically extractimplicit and vague event durations from text. Nº de ref. del artículo: 9783639127782
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