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| An Ontology-Based Information Retrieval Model for Vegetables E-Commerce |
| TAO Teng-yang, ZHAO Ming |
| College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P.R.China |
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摘要 With the rapid increment of the information on the web, traditional information retrieval based on the keywords is far from user’s satisfaction in recall and precision. In order to improve the recall ratio and the precision radio of IR engine in the vegetables e-commerce, an information retrieval model based on the vegetables e-commerce ontology is presented in this paper, vegetables e-commerce ontology was constructed by gathering and the analyzing vegetables e-commerce domain information on the web. The vegetables e-commerce ontology is composed of some kinds of vegetable classes and hierarchy relationship of vegetables classes. In the process of information retrieval, domain ontology helps to index information and information inference. An ontology-based information retrieval model is implemented, and which has more functions than the keyword-based web information retrieval engines. The experiment results show that the recall ratio and the precision ratio of ontology-based information retrieval model are higher than that of the information retrieval engine based on keyword at a certain extent.
Abstract With the rapid increment of the information on the web, traditional information retrieval based on the keywords is far from user’s satisfaction in recall and precision. In order to improve the recall ratio and the precision radio of IR engine in the vegetables e-commerce, an information retrieval model based on the vegetables e-commerce ontology is presented in this paper, vegetables e-commerce ontology was constructed by gathering and the analyzing vegetables e-commerce domain information on the web. The vegetables e-commerce ontology is composed of some kinds of vegetable classes and hierarchy relationship of vegetables classes. In the process of information retrieval, domain ontology helps to index information and information inference. An ontology-based information retrieval model is implemented, and which has more functions than the keyword-based web information retrieval engines. The experiment results show that the recall ratio and the precision ratio of ontology-based information retrieval model are higher than that of the information retrieval engine based on keyword at a certain extent.
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Received: 28 June 2011
Accepted:
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| Fund: This research is supported by the National High Technology Research and Development Program of China (2006AA10Z239). The authors are grateful for the anonymous reviewers who made constructive comments. |
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Corresponding Authors:
Correspondence ZHAO Ming, Tel: +86-10-62737855, E-mail: zhaoming@cau.edu.cn
E-mail: zhaoming@cau.edu.cn
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Cite this article:
TAO Teng-yang, ZHAO Ming.
2012.
An Ontology-Based Information Retrieval Model for Vegetables E-Commerce. Journal of Integrative Agriculture, 11(5): 800-807.
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| [1]Al-Jadir L, Parent C, Spaccapietra S. 2010. Reasoning with large ontologies stored in relational databases: the OntoMinD approach. Data and Knowledge Engineering, 69, 1158-1180. [2]Balhoff J P, Dahdul W M, Kothari C R, Lapp H. 2010. Phenex: Ontological annotation of phenotypic diversity. PLoS One, 5, 1-10. [3]Berners-Lee T, Hendler J, Lassila O. 2001. The semantic web: a new form of web content that is meaningful to computers will unleash a revolution of new possibilities, Scientific American, 285, 34-43. [4]Croft W. 1986. User-specified domain knowledge for document retrieval. In: SIGIR 1986 Proceedings of the 9th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, Pisa, Italy. pp. 201-206. [5]Deerwester S, Dumais S, Furnas G, Landauer T, Harshman R. 1990. Indexing by latent semantic analysis. Journal of the Society for Information Science, 41, 391-407. [6]Deng Z, Tang S, Zhang M. 2002. The ontology research summarizes. The Beijing University Journal: Natural Sciences Version, 38, 730-733. [7]Dumais S. 1990. Enhancing performance in latent semantic indexing retrieval, TM-ARH-017527. [8]Bellcore, USA. Fernández M, Cantador I, López V, Vallet D. 2011. Semantically enhanced information retrieval: An ontology-based approach. Web Semantics: Science, Services and Agents on the World Wide Web, 9, 434-452. [9]Gruber T. 1993. A translation approach to portable ontology specification. Knowledge Acquisition, 5, 199-220. [10]Guha R, McCool R, Miller E. 2003. Semantic search. In: Proceedings of the 12th International World Wide Web Conference (WWW 2003). Budapest, Hungary. pp. 700-709. [11]Guo Q, Zhang M. 2009. Semantic information integration and question answering based on pervasive agent ontology. Expert Systems with Applications, 36, 10068-10077. [12]Maedche A, Staab S, Stojanovic N, Studer R, Sure Y. 2003. SEmantic portAL: the SEAL approach. In: Proceedings of Spinning the Semantic Web. MIT Press, Cambridge London. pp. 317-359. [13]Mayfield J, Finin T. 2003. Information retrieval on the Semantic Web: Integrating inference and retrieval. In: Proceedings of SIGIR 2003 Semantic Web Workshop. Toronto, Canada. Muller H M, [14]Kenny E E, Paul W, Sternberg. 2004. An ontology-based information retrieval and extraction system for biological literature. PLoS Biology, 2, 1984-1998. [15]Niu Q, Qiu B, Xia S. 2008. Ontology-based learning resources in the field of semantic search model. Computer Application Research, 25, 1977-1982. [16]Noy N F, McGuinness D L. 2001. Ontology development 101: A guide to creating your first ontology. In: Stanford Knowledge Systems Laboratory Technical Report KSL-01-05. [17]Stanford Press, USA. Osterwalder A, Pigneur Y. 2002. An e-business model ontology for modeling e-business. In: Proceedings of the 15th Bled Electronic Commerce Conference e-Reality: Constructing the e-Economy. Bled, Slovenia. pp. 17-19. [18]Wang F, Zaniolo C. 2008. Temporal queries and version management in XML-based document archives. Data and Knowledge Engineering, 65, 304-324. [19]Zhou D, Bian J, Zheng S. 2008. Exploring social annotations for information retrieval. In: WWW 2008 Proceedings of the 17th International Conference on World Wide Web. Beijing, China. pp. 715-724. |
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