基于用户行为的启发式本体搜索机制

doi:10.3724/SP.J.1087.2012.02891

计算机应用 ›› 2012, Vol. 32 ›› Issue (10): 2891-2894.DOI: 10.3724/SP.J.1087.2012.02891

基于用户行为的启发式本体搜索机制

李江华¹,²,郑剑²

1. 北京科技大学国家材料服役安全科学中心,北京 100083
2. 江西理工大学信息工程学院,江西赣州 341000

收稿日期:2012-04-06 修回日期:2012-05-15 发布日期:2012-10-23 出版日期:2012-10-01
通讯作者: 李江华
作者简介:李江华（1976-）,男,河南新野人,副教授,博士研究生,主要研究方向：语义Web、数据工程；郑剑（1977-）,男,湖北黄石人,副教授,博士,主要研究方向：语义Web、软件工程。
基金资助:
国家”十一五”科技基础平台项目”材料科学数据共享网”

Heuristic ontology search mechanism based on user behavior

LI Jiang-hua¹,²,ZHENG Jian²

1. National Center for Materials Service Safety , University of Science and Technology Beijing, Beijing 100083, China
2. School of Information Engineering, Jiangxi University of Science and Technology,Ganzhou Jiangxi 341000, China

Received:2012-04-06 Revised:2012-05-15 Online:2012-10-23 Published:2012-10-01
Contact: LI Jiang-hua

摘要/Abstract

摘要： 为了能够以较高的准确率搜索到用户所需要的领域本体,在分析本体搜索需求和研究用户搜索行为的基础上,提出了一种基于用户行为的启发式本体搜索机制,利用不同用户由于领域认知不同,输入的具有领域共性的搜索关键词不同,实现用户搜索关键词的启发式扩展和搜索匹配度的提高。实验表明,使用该方法执行本体搜索具有较高的准确率和召回率。

关键词: 本体, 本体搜索, 关键词, 关键词扩展, 关键词搜索

Abstract: In order to search domain ontologies needed by users in higher precision, a heuristic ontology search mechanism was proposed on the basis of analyzing demands for ontology search and studying user search behavior, which took full advantages of the different search keywords belonging to same domain input by different users for their different domain knowledge to realize heuristic extension for users search keywords and improvement for search matching. The experimental results show that the proposed approach could help users to search and get the relevant ontologies at a higher precision and recall.

Key words: ontology, ontology search, keyword, keyword extension, keyword search

李江华郑剑. 基于用户行为的启发式本体搜索机制[J]. 计算机应用, 2012, 32(10): 2891-2894.

LI Jiang-hua ZHENG Jian. Heuristic ontology search mechanism based on user behavior[J]. Journal of Computer Applications, 2012, 32(10): 2891-2894.

参考文献

［1］ BERNERS-LEE, HENDLER J, LASSILA O. The semantic Web［J］. Scientific American, 2001,284(5):34-43. ［2］ ZHANG Y, VASCONCELOS W, SLEEMAN D. Ontosearch: An ontology search engine［C］ // Proceedings of 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Berlin: Springer-Verlag , 2004: 145-156. ［3］ DAML Ontology Library ［EB/OL］. ［2012-02-01］. http://www.daml.org/ontologies. ［4］ Ontolingua. Ontolingua ontology library［EB/OL］.［2012-03-25］. http://Ontolingua.stanford. edu. ［5］ Protégé. protege ontology library ［EB/OL］. ［2012-04-05］. http://protegewiki.stanford.edu/wiki/ Protege_Ontology_Library. ［6］ DING L, PAN R, FININ T. Finding and ranking knowledge on the semantic Web ［C］ // ISWC05: Proceedings of the 4th International Conference on the Semantic Web. Berlin: Springer-Verlag,2005:156-170. ［7］ ALANI H, BREWSTER C. Ontology ranking based on the analysis of concept structures ［C］ // Proceedings of the 3rd International Conference on Knowledge Capture.New York: ACM, 2005: 51-58. ［8］ PATEL C, SUPEKAR K, LEE Y,et al.OntoKhoj: A semantic Web portal for ontology searching, ranking, and classification［C］// Proceedings of 5th ACM International Workshop on Web Information and Data Management. New York: ACM, 2003: 58-61. ［9］ SHAH S A H, KHALID A, QADIR M A. OntoFecher:An approach for query generation to gather ontologies and ranking them by ensuring users context ［C］ // Proeeedings of the 4th International Conference on Emerging Technologies. Islamabad: [s.n.], 2008: 247-252. ［10］ TARTIR S, ARPINAR I B, MOORE M. OntoQA: Metric-based ontology quality analysis［C］ // Proeeedings of IEEE Workshop on Knowledge Acquisition from Distributed,Autonomous, Semantieally Heterogeneous Data and Knowledge Sourees. Houston: IEEE, 2005: 45-53. ［11］ JONES M, ALANI H. Content-based ontology ranking ［C］ // Proceedings of the 9th International Protégé Conference. Stanford, USA: Stanford University, 2006: 96-99. ［12］ PAN J Z, THOMAS E, SLEEMAN D. Ontosearch2: Searching and querying Web ontologies ［C］ // Proceedings of the IADIS International Conference. San Sebastian, Span: [s.n.], 2006:42-49. ［13］ AQUIN M, SABOU M, DZBOR M, et al. Watson: A gateway for the semantic Web ［C］ // Poster Session of the 4th European Semantic Web Conference. Innsbruck: ESWC, 2007:3-7. ［14］ CHENG G, GE W Y, QU Y Z. Falcons: Searching and browsing entities on the semantic Web［C］ // Proceedings of the 17th International Conference on World Wide Web. New York: ACM, 2008: 1101-1102. ［15］ BUITELAAR P, EIGNER T, DECLERCK T. OntoSelect: A dynamic ontology library with support for ontology selection［C］ // Proceedings of the Demo Session at the International Semantic Web Conference. New York: ACM, 2004: 54-57.

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基于用户行为的启发式本体搜索机制

Heuristic ontology search mechanism based on user behavior

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