2015•Computer Engineering and Applications JournalOpen access

Uygur ontology construction based on cross-language ontology reuse

LI Lianbe

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Abstract

Concerning the Uyghur-English ontology common knowledge acquisition problem, this paper proposes a Uyghur domain ontology fast-construction method based on cross-language ontology reuse. It converts the initial Uygur ontology into an English ontology, enriches it via some processes such as ontology search, mapping and merging, transforming it into Uygur domain ontology when reaches certain threshold. The conception such as data source cardinality, ontology cardinality and the data model for constructing Uyghur are proposed. A travel domain ontology with transformation rate reaching78.8% demonstrates the feasibility and effectiveness is constructed.

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What this paper is about

Concerning the Uyghur-English ontology common knowledge acquisition problem, this paper proposes a Uyghur domain ontology fast-construction method based on cross-language ontology reuse. It converts the initial Uygur ontology into an English ontology, enriches it via some processes such as ontology search, mapping and merging, transforming it into Uygur domain ontology when reaches certain threshold. The conception such as data source cardinality, ontology cardinality and the data model for constructing Uyghur are proposed. A travel domain ontology with transformation rate reaching78.8% demonstrates the feasibility and effectiveness is constructed.

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Available abstract

Concerning the Uyghur-English ontology common knowledge acquisition problem, this paper proposes a Uyghur domain ontology fast-construction method based on cross-language ontology reuse. It converts the initial Uygur ontology into an English ontology, enriches it via some processes such as ontology search, mapping and merging, transforming it into Uygur domain ontology when reaches certain threshold. The conception such as data source cardinality, ontology cardinality and the data model for constructing Uyghur are proposed. A travel domain ontology with transformation rate reaching78.8% demonstrates the feasibility and effectiveness is constructed.

Key concepts: Ontology, Process ontology, Upper ontology, Suggested Upper Merged Ontology, Ontology-based data integration, Computer science, Ontology Inference Layer, Ontology alignment

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