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Reasoning with the fuzzy description logic f-SHIN: Theory, practice and applications

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dc.contributor.author Stoilos, G en
dc.contributor.author Stamou, G en
dc.contributor.author Pan, JZ en
dc.contributor.author Simou, N en
dc.contributor.author Tzouvaras, V en
dc.date.accessioned 2014-03-01T02:45:46Z
dc.date.available 2014-03-01T02:45:46Z
dc.date.issued 2008 en
dc.identifier.issn 03029743 en
dc.identifier.uri http://hdl.handle.net/123456789/32376
dc.subject Fuzzy Description Logic en
dc.subject.other Description logic (DL) en
dc.subject.other Description Logics (DL) en
dc.subject.other Ontology languages en
dc.subject.other Reasoning algorithms en
dc.subject.other Reasoning systems en
dc.subject.other Semantic Web (SWeb) en
dc.subject.other Use cases en
dc.subject.other Data description en
dc.subject.other Inference engines en
dc.subject.other Information theory en
dc.subject.other Ontology en
dc.subject.other Programming theory en
dc.subject.other Semantic Web en
dc.subject.other Semantics en
dc.subject.other Fuzzy inference en
dc.title Reasoning with the fuzzy description logic f-SHIN: Theory, practice and applications en
heal.type conferenceItem en
heal.identifier.primary 10.1007/978-3-540-89765-1-16 en
heal.identifier.secondary http://dx.doi.org/10.1007/978-3-540-89765-1-16 en
heal.publicationDate 2008 en
heal.abstract The last couple of years it is widely acknowledged that uncertainty and fuzzy extensions to ontology languages, like Description Logics (DLs) and OWL, could play a significant role in the improvement of many Semantic Web (SW) applications. Many of the tasks of SW like trust, matching, merging, ranking usually involve confidence or truth degrees that one requires to represent and reason about. Fuzzy DLs are able to represent vague concepts such as a ""Tall"" person, a ""Hot"" place, a ""MiddleAged"" person, a ""near"" destination and many more. In the current paper we present a fuzzy extension to the DL SHIN. First, we present the semantics while latter a detailed reasoning algorithm that decides most of the key inference tasks of fuzzy-SHIN. Finally, we briefly present the fuzzy reasoning system FiRE, which implements the proposed algorithm and two use case scenarios where we have applied fuzzy DLs through FiRE. © 2008 Springer Berlin Heidelberg. en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.doi 10.1007/978-3-540-89765-1-16 en
dc.identifier.volume 5327 LNAI en
dc.identifier.spage 262 en
dc.identifier.epage 281 en


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