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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Ranganathan P. From microprocessors to nanostores: rethinking datacentric systems. IEEE Computer, 2011, 44(1): 39–48
Zhu Y Y, Zhong N, Xiong Y. Data explosion, data nature and dataology. In: Proceedings of International Conference on Brain Informatics. 2009, 147–158
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
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
Williams H E, Zobel J. Searchable words on the Web. International Journal on Digital Libraries, 2005, 5(2): 99–105
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
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
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
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
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
Carpineto C, Romano G. A survey of automatic query expansion in information retrieval. ACM Computing Surveys, 2012, 44(1): 1–50
Véronis J. Hyperlex: lexical cartography for information retrieval. Computer Speech & Language, 2004, 18(3): 223–252
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
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
Crestani F. Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 1997, 11(6): 453–482
Carpineto C, Romano G. Concept Data Analysis: Theory and Applications. Chichester: John Wiley & Sons, 2004
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
Melucci M. A basis for information retrieval in context. ACM Transactions on Information Systems, 2008, 26(3): 1–41
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
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
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
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
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
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 onWeblogs and Social Media. 2008, 10–18
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
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
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
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
Perugini S, Ramakrishnan N. Interacting withWeb hierarchies. IT Professional, 2006, 8(4): 19–28
Church K, Smyth B. Mobile content enrichment. In: Proceedings of the 12th International Conference on Intelligent User Interfaces. 2007, 112–121
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
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
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
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
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
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
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
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
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
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
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
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
Rocchio J J. Relevance feedback in information retrieval. The SMART Retrieval System-Experiments in Automatic Document Processing, 1971, 313–323
Robertson S E, Jones K S. Relevance weighting of search terms. Journal of the American Society for Information Science, 1976, 27(3): 129–146
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
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
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
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
Robertson S, Zaragoza H. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends® in Information Retrieval, 2009, 3(4): 333–389
Robertson S E. On term selection for query expansion. Journal of Documentation, 1990, 46(4): 359–364
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
Jurafsky D, Martin J H. Speech and Language Processing. Upper Saddle River, NJ: Pearson Prentice Hall, 2014
Author information
Authors and Affiliations
Corresponding author
Additional information
Ilyes Khennak is a PhD student in computer science at University of Sciences and Technology Houari Boumediene (USTHB), Algeria. He received his master degree in intelligent computer systems from USTHB in 2011. His research interests include artificial intelligence and information retrieval.
Habiba Drias received the MS degree in computer science from Case Western Reserve University, USA in 1984 and the PhD degree in computer science from University of Sciences and Technology Houari Boumediene (USTHB), Algeria in collaboration with UPMC, France in 1993. She is currently a full professor at USTHB since 1999 and directs the Laboratory of Research in Artificial Intelligence (LRIA). She has published around 200 papers in wellrecognized international conference proceedings and journals and has directed 20 PhD theses, 38 master theses and 31 engineer projects. In 2013, she won the Algerian Scopus award in computer science, and she was selected by a jury of international academicians as a founding member of the Algerian Academy of Science and Technology (AAST) in 2015.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Khennak, I., Drias, H. Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE. Front. Comput. Sci. 12, 163–176 (2018). https://doi.org/10.1007/s11704-016-5560-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-016-5560-0