Efficient Information Retrieval System Based on Semantics
Abstract
Information retrieval consisting of huge number of available documents on the Web makes finding relevant ones a challenging task. The quality of results that traditional keyword-based search provide is still not optimal for many types of user queries. Especially the semantic relation of the user query is handled inadequately by keyword-based search. The semantic based information retrieval can provide a solution for the above mentioned problem. This paper explains how semantic approach can be optimally exploited during the information retrieval process, and proposes a general framework by which efficient information retrieval can be made using semantic web approach. The framework can handle semantic based information retrieval by combining results from various approaches such as tokenization, Stop word removal, semantic checking and information retrieval. This allows integrating results from above mentioned approaches, and thus supports a gradual transition from classical keyword-based search to semantic-based ones.
Keywords
Full Text:
PDFReferences
Abdelali. A, Cowie. J, Soliman H.S. (2007), “Improving query precision using semantic expansion”, Information Processing and Management.
Berry, M.W. (1992), “Large scale singular value computations”, International Journal of Supercomputer Applications.
Hongwei Yang, “A document clustering algorithm for web search engine retrieval system”,2010 International Conference on e-Education, e-Business, e-Management and e-Learning.
Gang Lv, Cheng Zheng, Li Zhang, “Text information retrieval based on concept semantic similarity” 2009 Fifth International Conference on Semantics, Knowledge and Grid.
Jianpei Zhang, Zhongwei Li, Jing Yang, “A divisional incremental training algorithm of support vector machine” Mechatronics and Automation, 2005 IEEE Conference.
Jiuling Zhang, Beixing Deng, Xing Li, “Concept based query expansion using WordNet” 2009 International e-Conference on Advanced Science and Technology.
Kemafor Anyanwu, Angela Maduko, Amit Sheth 2005 “SemRank: Ranking Complex Relationship Search Results on the Semantic Web”.
Maria-Luiza Antonie, Osmar R. Za¨ıane University of Alberta, Canada “Text Document Categorization by Term Association”
Ming-Yen Chen, Hui-Chuan Chu, Yuh-Min Chen (2009), “Developing a semantic enable information retrieval mechanism”, Elsevier Journal on Expert Systems with Applications, May 2009.
Qinglin Guo, Ming Zhang (2007), “Multi- documents automation abstracting based on text clustering and semantic analysis”, Elsevier Journal on Knowledge based systems, 22, 482-485.
Trong Hai Duong, Geun Sik Jo, Ngoc Thanh Nguyen, “A method for integration across Text Corpus and WordNet based ontologies” 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.
Wei-Dong Fang, Ling Zhang, Yan-Xuan Wang, Shou-Bin Bong, “Toward a semantic search engine based on ontologies” Network Engineering and Resaerch center, South China University of Technology, Guanghou 510640, China.
Zhongcheng Zhao, “Measuring semantic similarity based on WordNet” 2009 Sixth Web Information Systems and Applications Conference.
Zongli Jiang and Changdong Lu, “A latent semantic analysis based method of getting the category attribute of words” 2009 International Conference on Electronic Computer Technology.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.