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Uncertainty and RuleML rulebases: A preliminary report

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dc.contributor.author Stoilos, G en
dc.contributor.author Stamou, G en
dc.contributor.author Tzouvaras, V en
dc.contributor.author Pan, JZ en
dc.date.accessioned 2014-03-01T02:43:43Z
dc.date.available 2014-03-01T02:43:43Z
dc.date.issued 2005 en
dc.identifier.issn 0302-9743 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/31497
dc.subject Fuzzy Set Theory en
dc.subject Markup Language en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.other Artificial intelligence en
dc.subject.other Computer programming languages en
dc.subject.other Fuzzy sets en
dc.subject.other Semantics en
dc.subject.other World Wide Web en
dc.subject.other XML en
dc.subject.other Fuzzy FOL RuleML en
dc.subject.other Rule Markup Language (RuleML) en
dc.subject.other RuleML rulebases en
dc.subject.other Vague knowledge en
dc.subject.other Computer architecture en
dc.title Uncertainty and RuleML rulebases: A preliminary report en
heal.type conferenceItem en
heal.identifier.primary 10.1007/11580072_17 en
heal.identifier.secondary http://dx.doi.org/10.1007/11580072_17 en
heal.language English en
heal.publicationDate 2005 en
heal.abstract Uncertainty, like imprecision and vagueness, has gained considerable attention the last decade. To this extend we present a preliminary report on extending the Rule Markup Language (RuleML) with fuzzy set theory, in order to be able to represent and handle vague knowledge. We also provide semantics for the case of fuzzy FOL RuleML. © Springer-Verlag Berlin Heidelberg 2005. en
heal.publisher SPRINGER-VERLAG BERLIN en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
heal.bookName LECTURE NOTES IN COMPUTER SCIENCE en
dc.identifier.doi 10.1007/11580072_17 en
dc.identifier.isi ISI:000234124600017 en
dc.identifier.volume 3791 LNCS en
dc.identifier.spage 199 en
dc.identifier.epage 203 en


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