A study on production of rules and facts for inference among resources in RDF/RDFS
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, H.-G. | - |
dc.contributor.author | Park, S.-J. | - |
dc.contributor.author | Cho, K.-D. | - |
dc.contributor.author | Kim, K.-T. | - |
dc.date.accessioned | 2023-03-09T01:13:47Z | - |
dc.date.available | 2023-03-09T01:13:47Z | - |
dc.date.issued | 2004-09 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65560 | - |
dc.description.abstract | This study proposes in the method for the prospective semantic inference in the web documents, catching the relation among RDF/RDFS resources, a core technique of the semantic web. For the semantic extraction, at first, we derive various kinds of facts caught intuitively from RDF/RDFS recommendation of W3C and stored them as facts in a knowledge base. Secondly, we produce rules from the various definitions of the recommendation and stored them as rules in a knowledge base, too. In this study, we choose the translation to the prolog, because the prolog is similar to the FOL representation. If there is a prolog compiler, we can easily confirm the inference process with the knowledge base. Rules and facts which represent the relation among the elements of the web documents will be helpful for semantic inference, provide the foundation available to agents. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.title | A study on production of rules and facts for inference among resources in RDF/RDFS | - |
dc.type | Article | - |
dc.identifier.bibliographicCitation | Proceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing, pp 147 - 152 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-11144321494 | - |
dc.citation.endPage | 152 | - |
dc.citation.startPage | 147 | - |
dc.citation.title | Proceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | First Order Logic(FOL) Prolog | - |
dc.subject.keywordAuthor | Intelligent agent | - |
dc.subject.keywordAuthor | Knowledge base | - |
dc.subject.keywordAuthor | RDF(Resource Definition Framework) and RDFS(RDF Schema) | - |
dc.subject.keywordAuthor | Semantic inference | - |
dc.subject.keywordAuthor | Semantic web | - |
dc.subject.keywordPlus | First Order Logic (FOL) Prolog | - |
dc.subject.keywordPlus | Knowledge base | - |
dc.subject.keywordPlus | RDF(resource definition framework) and RDFS(RDF Schema) | - |
dc.subject.keywordPlus | Semantic inference | - |
dc.subject.keywordPlus | Semantic web | - |
dc.subject.keywordPlus | Formal logic | - |
dc.subject.keywordPlus | Inference engines | - |
dc.subject.keywordPlus | Intelligent agents | - |
dc.subject.keywordPlus | Knowledge based systems | - |
dc.subject.keywordPlus | Knowledge representation | - |
dc.subject.keywordPlus | Semantics | - |
dc.subject.keywordPlus | XML | - |
dc.subject.keywordPlus | World Wide Web | - |
dc.description.journalRegisteredClass | scopus | - |
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