Bootstrapping-based Automatic Acquisition of Domain Concepts for Ontology Construction

YANG Yuehua; DU Junping; ZI Lingling

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 313-318

YANG Yuehua, DU Junping, ZI Lingling, “Bootstrapping-based Automatic Acquisition of Domain Concepts for Ontology Construction,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 313-318, 2013,

Citation:

YANG Yuehua, DU Junping, ZI Lingling, “Bootstrapping-based Automatic Acquisition of Domain Concepts for Ontology Construction,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 313-318, 2013,

YANG Yuehua, DU Junping, ZI Lingling, “Bootstrapping-based Automatic Acquisition of Domain Concepts for Ontology Construction,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 313-318, 2013,

Citation:

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Bootstrapping-based Automatic Acquisition of Domain Concepts for Ontology Construction

1.
Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2.
School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China

Funds: This work is supported by the National Basic Research Program of China (973 Program) (No.2012CB821200 No.2012CB821206), the National Natural Science Foundation of China (No.91024001, No.61070142), and Beijing Natural Science Foundation (No.4111002).

Received Date: 2012-04-01
Rev Recd Date: 2012-05-01
Publish Date: 2013-04-25

Abstract

Abstract

The domain concept is one of the key elements of ontology. In order to automatically acquire the domain concepts during the domain ontology construction, we proposed a novel Bootstrapping-based automatic acquisition algorithm of domain concepts. In our work, the compound words are extracted according to the combination conditions of the mutual information and the information entropy. The candidate domain concepts determinant conditions based on the co-occurrence sentences frequency are presented. Besides, to avoid omitting the domain concepts with the lower frequency or semantically similar to other domain concepts, the semantic factor is introduced. The experiments results have demonstrated that the compound domain concepts and the semantically similar domain concepts with the lower sentences frequency can also be extracted by using the proposed algorithm. And the proposed algorithm has obtained higher precision and recall, so it is effective and feasible.
- Bootstrapping,
- Automatic acquisition of concepts,
- Compound words extraction,
- Semantic factor

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References(18)

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