AASCIT - Journal - All Issues

ISSN Print: 2381-1110 ISSN Online: 2381-1129

American Journal of Computer Science and Information Engineering

The Journal

Manuscript Information

Automatic Query Expansion for Information Retrieval: A Survey and Problem Definition

American Journal of Computer Science and Information Engineering
Vol.4 , No. 3, Publication Date: Jul. 5, 2017, Page: 24-30

1311 Views Since July 5, 2017, 1171 Downloads Since Jul. 5, 2017

Paper in PDF (384K)

Follow on

Authors

[1]	Olalere A. Abass, Department of Computer Science, Tai Solarin College of Education, Ijebu Ode, Nigeria.
[2]	Olusegun Folorunso, Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria.
[3]	Babafemi O. Samuel, Department of Computer Science, Tai Solarin College of Education, Ijebu Ode, Nigeria.

Abstract

An ideal information retrieval system is expected to retrieve only the relevant documents while irrelevant ones are ignored towards ensuring throughput of the retrieval system and reduce the time user spend on the search engines as well as serving a motivation for continue the search. The process of IR consists of locating relevant documents on the basis of user query, such as keywords. One of the most fundamental research questions in information retrieval is how to operationally define the notion of relevance so that we can score a document with respect to a query appropriately. The most critical language issue for retrieval effectiveness is the term mismatch problem because both the indexers and the users do often not use the same words. This scenario is called vocabulary problem. Consequently, IRS users spend much time and resources to obtain their information need after querying the system. One solution to this problem is known as query expansion via pseudo relevance feedback which is intelligent technique for boosting the overall performance in IR. This paper reviews the intelligent method of query expansion and fashion out future work on the implementation of intelligent information retrieval for the purpose of removing “noise” (irrelevant documents) from the lists of retrieved documents.

Keywords

Search Engine, Pseudo Relevance Feedback, Fuzzy, Recall, Precision

Reference

[01]	Sharma Manish & Patel Rahul (2013). A Survey on Information Retrieval Models, Techniques and Applications. International Journal of Emerging Technology and Advanced Engineering, volume 3, Issue 11, pp. 542-545.
[02]	Mohameth-François Sy., Sylvie Ranwez., Jacky Montmain., Armelle Regnault., Michel Crampes & Vincent Ranwez Pezzoli (2012). User-centered and ontology based information Retrieval system for life sciences, BMC Bioinformatics, 1471-2105.
[03]	Akram Roshdi & Akram Roohparvar (2015). Review: Information Retrieval Techniques and Applications. International Journal of Computer Networks and Communications Security vol. 3, No. 9 pp. 373-377.
[04]	Silberschatz, A., Henry F. Korth. & S. Sudarshan (2011). Database System Concepts. 6th Edition. Published by McGraw-Hill. Pg 915-943.
[05]	Jessie Ooi, Xiuqin Ma, Hongwu Qin & Siau Chuin Liew (2015). A Survey of Query Expansion, Query Suggestion and Query Refinement Techniques. Proceedings of 4th International Conference on Software Engineering and Computer Systems (ICSECS), Kuantan, Pahang, Malaysia. pp. 112-117.
[06]	Xu, J. & Croft, W. B. (1996). Query expansion using local and global document analysis. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 4–11.
[07]	Carpineto, C. & Romano, G. (2012). A survey of automatic query expansion in information retrieval. ACM Computing Surveys, Vol. 44, No. 1, Article 1 pp.1-50.
[08]	Hiemstra Djoerd (2009). Information Retrieval Models. Published in Goker, A., and Davies, J. Information Retrieval: Searching in the 21st Century. John Wiley and Sons, Ltd., ISBN-13: 978-0470027622, Pg 1-2.
[09]	Rivas, A. R., Iglesias, E. L. & Borrajo, L. (2014). Study of Query Expansion Techniques and Their Application in the Biomedical Information Retrieval, The Scientific World Journal. Volume 2014, Article ID 132158 10 pages http://dx.doi.org/10.1155/2014/132158
[10]	Fan W, Gordon M. D, & Pathak P (2000) Personalization of search engine services for effective retrieval and knowledge management. Proceedings of International Conference on Information Systems (ICIS), Brisbane, Australia.
[11]	Rawat Poonam., Shri Prakash Dwivedi & Haridwari Lal Mandoria. (2014). An Adaptive Approach in Web Search Algorithm. International Journal of Information & Computation Technology. Vol. 4, No. 15, pp. 1575-1581.
[12]	Zhai, C. X. (2008). Statistical language models for information retrieval a critical review. Found. Trends Inf. Retr., 2:137-213, March.
[13]	Ermakova Liana & Mothe Josiane (2015). Query Expansion by Local Context Analysis. Insitut de Recherche en Informatique de Toulouse, Perm State National Research University.
[14]	Maitah Wafa., Mamoun. Al-Rababaa & Ghasan. Kannan (2013). Improving the Effectiveness of Information Retrieval System using Adaptive Genetic Algorithm. International Journal of Computer Science & Information Technology, Vol 5, No 5, pp. 91-105.
[15]	Croft, W. B. & Thompson, R. H. (1987). I3R: A new approach to the design of document retrieval systems. Journal of the American Society for Information Science, 38(6): 389-404.
[16]	Ye Zheng & Huang Jimmy Xiangji (2014). A Simple Term Frequency Transformation Model for Effective Pseudo Relevance Feedback. ACM SIGIR ’14, July 6-11, pp. 323-332.
[17]	Ahuja Sudhir & Goyal Rinkaj (2012). Information Retrieval in Intelligent Systems: Current Scenario & Issues. International Journal of Computer Engineering Science (IJCES), Volume 2 Issue 5, pp. 1-8.
[18]	Pasi Gabriella (2003). Intelligent Information Retrieval: Some Research Trends. Advances in Soft Computing, pp. 159-171, DOI: 10.1007/978-1-4471-3744-3_16
[19]	Zhou Dong, Mark Truran, Jianxun Liu & Sanrongf Zhang (2013). Collaborative Pseudo-Relevance Feedback. Expert Systems with Applications, 40, 6805-6812.
[20]	Singh J. & Sharan A. (2015) Context window based co-occurrence approach for improving feedback based query expansion in information retrieval. Int J Inform Retr Res 5(4):31–45.
[21]	Kankaria Ashish (2015). Query Expansion. Indian Institute of Technology Bombay, Mumbai.
[22]	Inkpen Diana (2010). Information Retrieval on the Internet. A Lecture Note, University of Ottawa, Canada, K1N 6N5, pp. 1-30.
[23]	Mitra, M., Singhal, A. & Buckley, C. (1998). Improving automatic query expansion. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 206–214.
[24]	Carpineto, C., Romano, G. & Giannini, V. (2002). Improving retrieval feedback with multiple term-ranking function combination. ACM Trans. Info. Syst. 20, 3, 259–290.
[25]	Liu, S., Liu, F., Yu, C., & Meng, W. (2004). An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 266–272.
[26]	Lee, K. S., Croft, W. B. & Allan, J. (2008). A cluster-based resampling method for pseudo-relevance feedback. In Proceedings of the 31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 235–242.
[27]	Cui, H., Wen, J. R., Nie J. Y. & Ma, W. Y. (2003). Query expansion by mining user logs. IEEE Trans. Knowl. Data Engin. 15, 4, 829–839.
[28]	Chu, W. W., Liu, Z. & Mao, W. (2002). Textual document indexing and retrieval via knowledge sources and data mining. Comm. Institute of Info. Comput. Machinery 5, 2.
[29]	Xu Yunjie (2005). Information Retrieval with a Hybrid Automatic Query Expansion and Data Fusion Procedure. Information Retrieval, 8, 41–65, 2005
[30]	Alhenshiri, Anwar A. (2013). Web Information Retrieval and Search Engines Techniques. Al-Satil Journal, PP: 55-92.
[31]	Bordogna Gloria & Pasi Gabriella (2001). Modelling vagueness in information retrieval. Lectures on Information Retrieval, volume 1980, pp. 207-241.