Multi-domain ontology mapping based on semantics
Ontology mapping indicates the semantic interconnection between the concepts of ontologies, while multi-domain ontology mapping is usually used to solve the semantic interconnection problem between domain ontologies. However, due to the differences in the definition approaches, there exists the hete...
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| Veröffentlicht in: | Cluster computing 2017-12, Vol.20 (4), p.3379-3391 |
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| creator | Song, Shengli Zhang, Xiang Qin, Guimin |
| description | Ontology mapping indicates the semantic interconnection between the concepts of ontologies, while multi-domain ontology mapping is usually used to solve the semantic interconnection problem between domain ontologies. However, due to the differences in the definition approaches, there exists the heterogeneity among the domain ontologies to a certain extent. This paper proposes a probability-based and similarity-based ontology mapping algorithm, the purpose of which is to calculate the similarity between the concepts of the multi-domain ontology. Using the ESA algorithm based on Wikipedia and the principle that the similarity between the concepts with the same name equals 1, the paper proposes a new concept, ontology mapping association graph, to represent mapping results. The experiments show that the accuracy rate of the probability-based and similarity-based ontology mapping algorithm can reach 80% on both two Chinese test sets, namely, WordSimilarity-353 and Words-240. Compared with other algorithms, it does stand out on the aspect of accuracy. |
| doi_str_mv | 10.1007/s10586-017-1087-x |
| format | Article |
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Zhang, Xiang ; Qin, Guimin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-84d7bca46713314dd199192503d7e1e60695b482cacbd3f695db4d0d29c1589a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Dynamic programming</topic><topic>Graphs</topic><topic>Heterogeneity</topic><topic>Interoperability</topic><topic>Knowledge</topic><topic>Knowledge representation</topic><topic>Language</topic><topic>Mapping</topic><topic>Online instruction</topic><topic>Ontology</topic><topic>Operating Systems</topic><topic>Processor Architectures</topic><topic>Semantic web</topic><topic>Semantics</topic><topic>Similarity</topic><topic>Software</topic><topic>World Wide Web</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Shengli</creatorcontrib><creatorcontrib>Zhang, Xiang</creatorcontrib><creatorcontrib>Qin, Guimin</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Proquest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Cluster computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Shengli</au><au>Zhang, Xiang</au><au>Qin, Guimin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-domain ontology mapping based on semantics</atitle><jtitle>Cluster computing</jtitle><stitle>Cluster Comput</stitle><date>2017-12-01</date><risdate>2017</risdate><volume>20</volume><issue>4</issue><spage>3379</spage><epage>3391</epage><pages>3379-3391</pages><issn>1386-7857</issn><eissn>1573-7543</eissn><abstract>Ontology mapping indicates the semantic interconnection between the concepts of ontologies, while multi-domain ontology mapping is usually used to solve the semantic interconnection problem between domain ontologies. However, due to the differences in the definition approaches, there exists the heterogeneity among the domain ontologies to a certain extent. This paper proposes a probability-based and similarity-based ontology mapping algorithm, the purpose of which is to calculate the similarity between the concepts of the multi-domain ontology. Using the ESA algorithm based on Wikipedia and the principle that the similarity between the concepts with the same name equals 1, the paper proposes a new concept, ontology mapping association graph, to represent mapping results. The experiments show that the accuracy rate of the probability-based and similarity-based ontology mapping algorithm can reach 80% on both two Chinese test sets, namely, WordSimilarity-353 and Words-240. Compared with other algorithms, it does stand out on the aspect of accuracy.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10586-017-1087-x</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-6879-0027</orcidid></addata></record> |
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| language | eng |
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| subjects | Algorithms Computer Communication Networks Computer Science Dynamic programming Graphs Heterogeneity Interoperability Knowledge Knowledge representation Language Mapping Online instruction Ontology Operating Systems Processor Architectures Semantic web Semantics Similarity Software World Wide Web |
| title | Multi-domain ontology mapping based on semantics |
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