Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE

doi:10.1007/s11704-016-5560-0

Front. Comput. Sci.

2018, Vol. 12

Issue (1) : 163-176 https://doi.org/10.1007/s11704-016-5560-0

RESEARCH ARTICLE

Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE

Ilyes KHENNAK(

), Habiba DRIAS

Laboratory for Research in Artificial Intelligence, Computer Science Department, University of Sciences and Technology Houari Boumediene (USTHB), Algiers 16111, Algeria

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Abstract

Because of users’ growing utilization of unclear and imprecise keywords when characterizing their information need, it has become necessary to expand their original search queries with additional words that best capture their actual intent. The selection of the terms that are suitable for use as additional words is in general dependent on the degree of relatedness between each candidate expansion term and the query keywords. In this paper, we propose two criteria for evaluating the degree of relatedness between a candidate expansion word and the query keywords: (1) co-occurrence frequency, where more importance is attributed to terms occurring in the largest possible number of documents where the query keywords appear; (2) proximity, where more importance is assigned to terms having a short distance from the query terms within documents. We also employ the strength Pareto fitness assignment in order to satisfy both criteria simultaneously. The results of our numerical experiments on MEDLINE, the online medical information database, show that the proposed approach significantly enhances the retrieval performance as compared to the baseline.

Keywords information retrieval query expansion pseudorelevance feedback proximity multi-objective optimization Pareto dominance MEDLINE

Corresponding Author(s): Ilyes KHENNAK

Just Accepted Date: 28 September 2016 Online First Date: 08 December 2017 Issue Date: 12 January 2018

Cite this article:

Ilyes KHENNAK,Habiba DRIAS. Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE[J]. Front. Comput. Sci., 2018, 12(1): 163-176.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5560-0
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I1/163

1	Ranganathan P. From microprocessors to nanostores: rethinking datacentric systems. IEEE Computer, 2011, 44(1): 39–48 https://doi.org/10.1109/MC.2011.18
2	Zhu Y Y, Zhong N, Xiong Y. Data explosion, data nature and dataology. In: Proceedings of International Conference on Brain Informatics. 2009, 147–158 https://doi.org/10.1007/978-3-642-04954-5_25
3	Ntoulas A, Cho J, Olston C. What’s new on the Web?: the evolution of the Web from a search engine perspective. In: Proceedings of the 13th International Conference on World Wide Web. 2004, 1–12 https://doi.org/10.1145/988672.988674
4	Bharat K, Broder A. A technique for measuring the relative size and overlap of public web search engines. Computer Networks and ISDN Systems, 1998, 30(1): 379–388 https://doi.org/10.1016/S0169-7552(98)00127-5
5	Williams H E, Zobel J. Searchable words on the Web. International Journal on Digital Libraries, 2005, 5(2): 99–105 https://doi.org/10.1007/s00799-003-0050-z
6	Eisenstein J, O’Connor B, Smith N A, Xing E P. Mapping the geographical diffusion of new words. In: Proceedings of Workshop on Social Network and Social Media Analysis: Methods, Models and Applications. 2012
7	Sun H M. A study of the features of internet english from the linguistic perspective. Studies in Literature and Language, 2010, 1(7): 98–103
8	Chen Q, Li M, Zhou M. Improving query spelling correction usingWeb search results. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2007, 181–189
9	Subramaniam L V, Roy S, Faruquie T A, Negi S. A survey of types of text noise and techniques to handle noisy text. In: Proceedings of the 3rd Workshop on Analytics for Noisy Unstructured Text Data. 2009, 115–122 https://doi.org/10.1145/1568296.1568315
10	Ahmad F, Kondrak G. Learning a spelling error model from search query logs. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005, 955–962 https://doi.org/10.3115/1220575.1220695
11	Carpineto C, Romano G. A survey of automatic query expansion in information retrieval. ACM Computing Surveys, 2012, 44(1): 1–50 https://doi.org/10.1145/2071389.2071390
12	Véronis J. Hyperlex: lexical cartography for information retrieval. Computer Speech & Language, 2004, 18(3): 223–252 https://doi.org/10.1016/j.csl.2004.05.002
13	Bernardini A, Carpineto C, Amico M D. Full-subtopic retrieval with keyphrase-based search results clustering. In: Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technologies. 2009, 206–213 https://doi.org/10.1109/WI-IAT.2009.37
14	Wong S K M, Ziarko W, Raghavan V V, Wong P. On modeling of information retrieval concepts in vector spaces. ACM Transactions on Database Systems, 1987, 12(2): 299–321 https://doi.org/10.1145/22952.22957
15	Crestani F. Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 1997, 11(6): 453–482 https://doi.org/10.1023/A:1006569829653
16	Carpineto C, Romano G. Concept Data Analysis: Theory and Applications. Chichester: John Wiley & Sons, 2004 https://doi.org/10.1002/0470011297
17	Sahlgren M. An introduction to random indexing. In: Proceedings of Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering. 2005
18	Melucci M. A basis for information retrieval in context. ACM Transactions on Information Systems, 2008, 26(3): 1–41 https://doi.org/10.1145/1361684.1361687
19	Sun R, Ong C H, Chua T S. Mining dependency relations for query expansion in passage retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 382–389 https://doi.org/10.1145/1148170.1148237
20	Schlaefer N, Ko J, Betteridge J, Pathak M A, Nyberg E, Sautter G. Semantic extensions of the Ephyra QA system for TREC 2007. In: Proceedings of the 16th Text REtrieval Conference. 2007
21	Kraaij W, Nie J Y, Simard M. Embedding Web-based statistical translation models in cross-language information retrieval. Computational Linguistics, 2003, 29(3): 381–419 https://doi.org/10.1162/089120103322711587
22	Kherfi M L, Ziou D, Bernardi A. Image retrieval from the World Wide Web: issues, techniques, and systems. ACM Computing Surveys, 2004, 36(1): 35–67 https://doi.org/10.1145/1013208.1013210
23	Natsev A P, Haubold A, Tešić J, Xie L X, Yan R. Semantic conceptbased query expansion and re-ranking for multimedia retrieval. In: Proceedings of the 15th ACM International Conference on Multimedia. 2007, 991–1000
24	Arguello J, Elsas J L, Callan J, Carbonell J G. Document representation and query expansion models for blog recommendation. In: Proceedings of the 2nd International Conference on Weblogs and Social Media. 2008, 10–18
25	Hidalgo J M G, de Buenaga Rodríguez M, Pérez J C C. The role of word sense disambiguation in automated text categorization. In: Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems. 2005, 298–309
26	Graupmann J, Cai J, Schenkel R. Automatic query refinement using mined semantic relations. In: Proceedings of International Workshop on Challenges in Web Information Retrieval and Integration. 2005, 205–213 https://doi.org/10.1109/WIRI.2005.12
27	Kamvar M, Baluja S. The role of context in query input: using contextual signals to complete queries on mobile devices. In: Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services. 2007, 405–412 https://doi.org/10.1145/1377999.1378046
28	Huang C C, Lin K M, Chien L F. Automatic training corpora acquisition through Web mining. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technologies. 2005, 193–199
29	Perugini S, Ramakrishnan N. Interacting withWeb hierarchies. IT Professional, 2006, 8(4): 19–28 https://doi.org/10.1109/MITP.2006.91
30	Church K, Smyth B. Mobile content enrichment. In: Proceedings of the 12th International Conference on Intelligent User Interfaces. 2007, 112–121 https://doi.org/10.1145/1216295.1216320
31	Macdonald C, Ounis I. Expertise drift and query expansion in expert search. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management. 2007, 341–350 https://doi.org/10.1145/1321440.1321490
32	Billerbeck B, Zobel J. Document expansion versus query expansion for ad-hoc retrieval. In: Proceedings of the 10th Australasian Document Computing Symposium. 2005, 34–41
33	Shokouhi M, Azzopardi L, Thomas P. Effective query expansion for federated search. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 427–434 https://doi.org/10.1145/1571941.1572015
34	Wang H, Liang Y, Fu L, Xue G R, Yu Y. Efficient query expansion for advertisement search. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 51–58 https://doi.org/10.1145/1571941.1571953
35	Voorhees E M. Query expansion using lexical-semantic relations. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1994, 61–69 https://doi.org/10.1007/978-1-4471-2099-5_7
36	Collins-Thompson K, Callan J. Query expansion using random walk models. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. 2005, 704–711 https://doi.org/10.1145/1099554.1099727
37	Liu S, Liu F, Yu C, Meng W Y. 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. 2004, 266–272 https://doi.org/10.1145/1008992.1009039
38	Song M, Song I Y, Hu X H, Allen R B. Integration of association rules and ontologies for semantic query expansion. Data & Knowledge Engineering, 2007, 63(1): 63–75 https://doi.org/10.1016/j.datak.2006.10.010
39	Gauch S, Wang J Y, Rachakonda S M. A corpus analysis approach for automatic query expansion and its extension to multiple databases. ACM Transactions on Information Systems, 1999, 17(3): 250–269 https://doi.org/10.1145/314516.314519
40	Hu J N, Deng W H, Guo J. Improving retrieval performance by global analysis. In: Proceedings of the 18th International Conference on Pattern Recognition. 2006, 703–706
41	Park L A, Ramamohanarao K. Query expansion using a collection dependent probabilistic latent semantic thesaurus. In: Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 2007, 224–235 https://doi.org/10.1007/978-3-540-71701-0_24
42	Milne D N, Witten I H, Nichols D M. A knowledge-based search engine powered by wikipedia. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management. 2007, 445–454 https://doi.org/10.1145/1321440.1321504
43	Rocchio J J. Relevance feedback in information retrieval. The SMART Retrieval System-Experiments in Automatic Document Processing, 1971, 313–323
44	Robertson S E, Jones K S. Relevance weighting of search terms. Journal of the American Society for Information Science, 1976, 27(3): 129–146 https://doi.org/10.1002/asi.4630270302
45	Wong W, Luk R W P, Leong H V, Ho K, Lee D L. Re-examining the effects of adding relevance information in a relevance feedback environment. Information Processing & Management, 2008, 44(3): 1086–1116 https://doi.org/10.1016/j.ipm.2007.12.002
46	Zhai C X, Lafferty J. Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the 10th International Conference on Information and Knowledge Management. 2001, 403–410 https://doi.org/10.1145/502585.502654
47	Lavrenko V, Croft W B. Relevance based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2001, 120–127 https://doi.org/10.1145/383952.383972
48	Khennak I, Drias H. Strength pareto fitness assignment for generating expansion features. In: Proceedings of the 3rd World Conference on Information Systems and Technologies. 2015, 133–142 https://doi.org/10.1007/978-3-319-16486-1_13
49	Robertson S, Zaragoza H. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends® in Information Retrieval, 2009, 3(4): 333–389
50	Robertson S E. On term selection for query expansion. Journal of Documentation, 1990, 46(4): 359–364 https://doi.org/10.1108/eb026866
51	Carpineto C, De Mori R, Romano G, Bigi B. An information-theoretic approach to automatic query expansion. ACM Transactions on Information Systems, 2001, 19(1): 1–27 https://doi.org/10.1145/366836.366860
52	Jurafsky D, Martin J H. Speech and Language Processing. Upper Saddle River, NJ: Pearson Prentice Hall, 2014

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