Exploiting affinity propagation for automatic acquisition of domain concept in ontology learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Qasim, Iqbal | - |
dc.contributor.author | Jeong, Jin woo | - |
dc.contributor.author | Khan, Sharifullah | - |
dc.contributor.author | Lee, Dong ho | - |
dc.date.accessioned | 2021-06-23T12:04:48Z | - |
dc.date.available | 2021-06-23T12:04:48Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2011-09 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/39135 | - |
dc.description.abstract | Semantic Web uses domain ontology to bridge the gap among the members of a domain through minimization of conceptual and terminological incompatibilities. However, several barriers must be overcome before domain ontology becomes a practical and useful tool. One important issue is identification and selection of domain concepts for domain ontology learning when several hundreds or even thousands of terms are extracted and available from relevant text documents shared among the members of a domain. We present a novel domain concept acquisition and selection approach for ontology learning that uses affinity propagation algorithm, which takes as input semantic and structural similarity between pairs of extracted terms called data points. Real-valued messages are passed between data points (terms) until high quality set of exemplars (concepts) and cluster iteratively emerges. All exemplars will be considered as domain concepts for learning domain ontologies. Our empirical results show that our approach achieves high precision and recall in selection of domain concepts using less number of iterations. © 2011 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | Exploiting affinity propagation for automatic acquisition of domain concept in ontology learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Dong ho | - |
dc.identifier.doi | 10.1109/ICET.2011.6048471 | - |
dc.identifier.scopusid | 2-s2.0-80755176733 | - |
dc.identifier.bibliographicCitation | 2011 7th International Conference on Emerging Technologies, ICET 2011 | - |
dc.relation.isPartOf | 2011 7th International Conference on Emerging Technologies, ICET 2011 | - |
dc.citation.title | 2011 7th International Conference on Emerging Technologies, ICET 2011 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 3 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.subject.keywordPlus | Affinity propagation | - |
dc.subject.keywordPlus | Automatic acquisition | - |
dc.subject.keywordPlus | Data points | - |
dc.subject.keywordPlus | Domain concepts | - |
dc.subject.keywordPlus | Domain ontologies | - |
dc.subject.keywordPlus | Empirical results | - |
dc.subject.keywordPlus | High precision | - |
dc.subject.keywordPlus | High quality | - |
dc.subject.keywordPlus | Novel domain | - |
dc.subject.keywordPlus | Number of iterations | - |
dc.subject.keywordPlus | ontology construction | - |
dc.subject.keywordPlus | ontology learning | - |
dc.subject.keywordPlus | Structural similarity | - |
dc.subject.keywordPlus | Text document | - |
dc.subject.keywordPlus | Semantic Web | - |
dc.subject.keywordPlus | Semantics | - |
dc.subject.keywordPlus | User interfaces | - |
dc.subject.keywordPlus | Ontology | - |
dc.subject.keywordAuthor | affinity propagation | - |
dc.subject.keywordAuthor | domain concepts | - |
dc.subject.keywordAuthor | ontology construction | - |
dc.subject.keywordAuthor | ontology learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/6048471 | - |
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