Publication:
Improving knowledge extraction from texts by generating possible relations

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dc.Conferencecode136514
dc.Conferencedate25 October 2017 through 27 October 2017
dc.Conferencename2017 World Congress on Engineering and Computer Science, WCECS 2017
dc.FundingDetailsUniversiti Sains Islam Malaysia: PPP/USG-0116/FST/30/11616
dc.FundingDetailsManuscript received July 24, 2017; revised August 2, 2017. This work was supported in part by the Universiti Sains Islam Malaysia under Grant PPP/USG-0116/FST/30/11616.
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversiti Putra Malaysia (UPM)
dc.contributor.affiliationsUniversiti Tun Hussein Onn Malaysia (UTHM)
dc.contributor.authorNabila N.F.en_US
dc.contributor.authorBasir N.en_US
dc.contributor.authorMamat A.en_US
dc.contributor.authorDeris M.M.en_US
dc.date.accessioned2024-05-28T08:32:06Z
dc.date.available2024-05-28T08:32:06Z
dc.date.issued2017
dc.description.abstractExisting research focus on extracting the concepts and relations within a single sentence or in subject-object-object pattern. However, a problem arises when either the object or subject of a sentence is "missing" or "uncertain", which will cause the domain texts to be improperly presented as the relationship between concepts is no extracted. This paper proposes a solution for the enrichment of the knowledge of domain text by finding all possible relations. The proposed method suggests the appropriate or the most likely term for an uncertain subject or object of a sentence using the probability theory. In addition, the method can extract the relations between concepts (i.e. subject and object) that appear not only in a single sentence, but also in different sentences by using a synonym of the predicates. The proposed method has been tested and evaluated with a collection of domain texts that describe tourism. Precision, recall, and f-score metrics have been used to evaluate the results of the experiments. � Copyright International Association of Engineers.
dc.description.natureFinalen_US
dc.editorAo S.I.Grundfest W.S.Douglas C.en_US
dc.identifier.epage60
dc.identifier.isbn9789880000000
dc.identifier.issn20780958
dc.identifier.scopus2-s2.0-85055057820
dc.identifier.spage55
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85055057820&partnerID=40&md5=b8fa89eff5429262c578488bd7b9af90
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9009
dc.identifier.volume1
dc.languageEnglish
dc.language.isoen_US
dc.publisherNewswood Limiteden_US
dc.relation.ispartofLecture Notes in Engineering and Computer Science
dc.sourceScopus
dc.subjectNon-taxonomicen_US
dc.subjectOntologyen_US
dc.subjectRelation Extractionen_US
dc.subjectOntologyen_US
dc.subjectProbabilityen_US
dc.subjectKnowledge extractionen_US
dc.subjectMost likelyen_US
dc.subjectNon-taxonomicen_US
dc.subjectObject patternsen_US
dc.subjectProbability theoryen_US
dc.subjectRelation extractionen_US
dc.subjectRelationship between conceptsen_US
dc.subjectResearch focusen_US
dc.subjectExtractionen_US
dc.titleImproving knowledge extraction from texts by generating possible relations
dc.typeConference Paperen_US
dspace.entity.typePublication

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