Abstract
Nowadays, searching the relevant documents from a large dataset becomes a big challenge. Automatic query expansion is one of the techniques, which addresses this problem by refining the query. A new query expansion approach using cuckoo search and accelerated particle swarm optimization technique is proposed in this paper. The proposed approach mainly focused to find the most relevant expanded query rather than suitable expansion terms. In this paper, Fuzzy logic is also employed, which improves the performance of accelerated particle swarm optimization by controlling various parameters. We have compared the proposed approach with other existing and recently developed automatic query expansion approaches on various evaluating parameters such as average recall, average precision, Mean-Average Precision, F-measure and precision-recall graph. We have evaluated the performance of all approaches on three datasets CISI, CACM and TREC-3. The results obtained for all three datasets depict that the proposed approach gets better results in comparison to other automatic query expansion approaches.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Barathi M, Valli S (2013) Query disambiguation using clustering and concept based semantic web search for efficient information retrieval. Life Sci. J. 10(2):147–155. https://doi.org/10.7537/marslsj100213.23
Ben HE, Ounis I (2003) A study of parameter tuning for term frequency normalization. In: Proceedings of the twelfth international conference on information and knowledge management. ACM Press, New Orleans, pp 10–16. https://doi.org/10.1145/956863.956867
Bendersky M, Metzler D, Croft BW (2012) Effective query expansion with multiple information sources. fifth ACM international conference on web search and data mining, USA, pp 1–10. https://doi.org/10.1145/2124295.2124349
Bhatnagar P, Pareek N (2015) Genetic algorithm-based query expansion for improved information retrieval. In: proceedings of the international conference on intelligent computing, communication and devices, pp 47–55. https://doi.org/10.1007/978-81-322-2012-1_6
Billerbeck B, Scholer F, Williams HE, Zobel J (2003) Query expansion using associated queries. In: proceedings of the 12th international conference on information and knowledge management, New Orleans, pp 2–9. https://doi.org/10.1145/956863.956866
Carlos M, Maguitman A (2009) A semi-supervised incremental algorithm to automatically formulate topical queries. Inf Sci 179:1881–1892. https://doi.org/10.1016/j.ins.2009.01.029
Carpineto C, Romano G (2012) A survey of automatic query expansion in information retrieval. ACM Computer Survey 44(1):1–50. https://doi.org/10.1145/2071389.2071390
Chang Y, Chen C (2006) A new query reweighting method for document retrieval based on genetic algorithms. IEEE Trans Evolut Comput 10(5):617–622. https://doi.org/10.1109/TEVC.2005.863130
Chang Y, Chen S, Liau C (2007) A new query expansion method for document retrieval based on the inference of fuzzy rules. J Chin Inst Eng 30(3):511–515. https://doi.org/10.1080/02533839.2007.9671279
Chen H, Yu J, Furuse K, Ohbo N (2001) Support IR query refinement by partial keyword set. In: proceedings of the second international conference on web information systems engineering, Singapore, 11, pp 245–253. https://doi.org/10.1109/WISE.2001.996485
Cooper JW, Byrd R (1998) OBIWAN—a visual interface for prompted query refinement. In: proceedings of the 31st Hawaii international conference on system sciences, Hawaii, 2, pp 277–285. https://doi.org/10.1109/HICSS.1998.651710
Cur´e OC, Maurer H, Shah NH, Le Pendu P (2015) A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest. BMC Med Inform Decision Making 15(1):1–6. https://doi.org/10.1186/1472-6947-15-S1-S8
Enireddy V, Reddi KK (2015) Improved cuckoo search with particle swarm optimization for classification of compressed images. Sadhana Indian Acad Sci 40(8):2271–2285
Fattahi R, Concepcio´n SW, Cole F (2008) An alternative approach to natural language query expansion in search engines: text analysis of non-topical terms in web documents. Inf Process Manage 44:1503–1516. https://doi.org/10.1016/j.ipm.2007.09.009
Gao Y, Zhang G, Ma J, Lu J (2010) A λ-cut and goal-programming-based algorithm for fuzzy-linear multiple-objective bilevel optimization. IEEE Trans Fuzzy Syst 18(1):1–13. https://doi.org/10.1109/TFUZZ.2009.2030329
Gong Z, Cheang C, Hou L (2006) Multi-term web query expansion using WordNet. Database and expert systems applications. Lect Notes Comput Sci 4080(388.):379 https://doi.org/10.1007/11827405_37
Grootjen FA, Weide TP (2006) Conceptual query expansion. Data Knowl Eng 56:174–193. https://doi.org/10.1016/j.datak.2005.03.006
Gupta Y, Saini A (2017) A novel Fuzzy-PSO term weighting automatic query expansion approach using semantic filtering. Knowl Based Syst 136:97–120. https://doi.org/10.1016/j.knosys.2017.09.004
Gupta Y, Saini A, Saxena AK (2015) A new fuzzy logic based ranking function for efficient information retrieval system. Expert Syst Appl 42: 1223–1234. https://doi.org/10.1016/j.eswa.2014.09.009
Gupta PK, Lal S, Kiran MS (2018) Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0891-3
Horng J, Yeh C (2000) Applying genetic algorithms to query optimization in document retrieval. Inf Process Manage 36:737–759. https://doi.org/10.1016/S0306-4573(00)00008-X
Ibrahim RA, Ewees AA, Oliva D, Elaziz MA, Lu S (2018) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-1031-9
Kabary IA, Schuldt H (2014) Enhancing sketch-based sport video retrieval by suggesting relevant motion paths. In: proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, pp 1227– 1230. https://doi.org/10.1145/2600428.2609551
Khennak I, Drias H (2016) Bat algorithm for efficient query expansion: application to MEDLINE. In: proceedings of the 4th World conference on information systems and technologies, pp 113–122. https://doi.org/10.1007/978-3-319-31232-3_11
Khennak I, Drias H (2017) An accelerated PSO for query expansion in web information retrieval: application to medical dataset. Appl Intell 47(3): 793–808. https://doi.org/10.1007/s10489-017-0924-1
Kim BM, Kim JY, Kim J (2001) Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference. In: proceedings of the joint ninth IFSA world congress and 20th NAFIPS international conference, Vancouver, 2, pp 715–720. https://doi.org/10.1109/NAFIPS.2001.944690
Latiri C, Haddad H, Hamrouni T (2012) Towards an effective automatic query expansion process using an association rule mining approach. J Intell Inf Syst 39(1):209–247. https://doi.org/10.1007/s10844-011-0189-9
Lee Y, Bang S (2018) Improved image retrieval and classification with combined invariant features and color descriptor. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0817-0
Leturia I, Gurrutxaga A, Areta N, Alegria I, Ezeiza A (2013) Morphological query expansion and language-filtering words for improving Basque web retrieval. Lang Resour Evaluat 47(2):425–448. https://doi.org/10.1007/s10579-012-9208-x
Li Q, Tian M, Liu J, Sun J (2016) An implicit relevance feedback method for CBIR with real-time eye tracking. Multimed Tools Appl 75(5):2595–2611. https://doi.org/10.1007/s11042-015-2873-1
Lin HC, Wang LH, Chen SM (2006) Query expansion for document retrieval based on fuzzy rules and user relevance feedback techniques. Expert Syst Appl 31:397–405. https://doi.org/10.1016/j.eswa.2005.09.078
Lin HC, Wang LH, Chen SM (2008) A new query expansion method for document retrieval by mining additional query terms. Inf Manag Sci 19(1):17–30
Nowacka K, Zadrozny S, Kacprzyk J (2008) A new fuzzy logic based information retrieval model. In: proceeding of IPMU’08, pp 1749–1756. http://www.gimac.uma.es/ipmu08/proceedings/papers/234-Zadrozni.pdf
Oh HS, Jung Y (2015) Cluster-based query expansion using external collections in medical information retrieval. J Biomed Inform 58:70–79. https://doi.org/10.1016/j.jbi.2015.09.017
Park JH, Croft WB (2015) Using key concepts in a translation model for retrieval. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM New York, pp 927–930. https://doi.org/10.1145/2766462.2767768
Qian B, Wang Q, Hu R, Zhou Z, Yu C, Zhou Z (2017) An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0667-1
Ramalingam G, Dhandapani S (2014) A novel adaptive cuckoo search for optimal query plan generation. Sci World J 2014:1–7. https://doi.org/10.1155/2014/727658
Rijsbergen C (1979) Information Retrieval, 2 ed., Butterworth, Houston
Rivas A, Iglesias E, Borrajo L (2014) Study of query expansion techniques and their application in the biomedical information retrieval. Sci World J 2014:1–10. https://doi.org/10.1155/2014/132158
Robertson S, Jones S (1976) Relevance weighting of search terms. J Am Soc Inf Sci 27:129–145. https://doi.org/10.1002/asi.4630270302
Robertson A, Willet P (1996) An upperbound to the performance for ranked-output searching: optimal weighting of query terms using a genetic algorithm. J Doc 52(4):405–420. https://doi.org/10.1108/eb026973
Saeedeh MD, Siddiqi J, Zadeh Y, Rahman F (2012) Adaptive information retrieval system via modelling user behavior. J Ambient Intell Humaniz Comput 5(1):105–110. https://doi.org/10.1007/s12652-012-0138-7
Sanchez E, Miyano H, Brachet J (1995) Optimization of fuzzy queries with genetic algorithms. In: proceedings of Applications to a data base of patents in biomedical engineering, VI IFSA Congress, Sao-Paulo, Brazil, pp 293–296
Saraiva PC, Cavalcanti JM, de Moura ES, Gon˙calves MA, Torres RDS (2016) A multimodal query expansion based on genetic programming for visually-oriented e-commerce applications. Inf Process Manag 52(5):783–800. https://doi.org/10.1016/j.ipm.2016.03.001
Singh J, Sharan A (2015) Relevance Feedback Based Query Expansion Model Using Borda Count and Semantic Similarity Approach. Comput Intell Neurosci 2015(568197):1–13. https://doi.org/10.1155/2015/568197
Singh J, Sharan A (2016) Relevance Feedback-based Query Expansion Model using Ranks Combining and Word2Vec Approach. Journal of IETE Journal of Research 62(5):591–604. https://doi.org/10.1080/03772063.2015.1136575
Singh J, Sharan A (2017a) A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach. Journal Neural Computing Applications 28(9):2557–2580. https://doi.org/10.1007/s00521-016-2207-x
Singh J, Sharan A (2018) Rank fusion and semantic genetic notion based automatic query expansion model. Swarm Evolut Comput 38: 295–308. https://doi.org/10.1016/j.swevo.2017.09.007
Singh J, Sharan A, Saini M (2017b) Term co-occurrence and context window-based combined approach for query expansion with the semantic notion of terms. International Journal of Web Science 3(1):32–57. https://doi.org/10.1504/IJWS.2017.088677
Suganthan P (1999) Particle swarm optimizer with neighborhood operator. In: Proceedings of IEEE International Conference on Evolutionary Computation, 3, pp 1958–1962. https://doi.org/10.1109/CEC.1999.785514
Tayal DK, Sabharwal S, Jain A, Mittal K (2012) Intelligent query expansion for the queries including numerical terms. In: Proceedings of National Conference on Communication Technologies and its impact on Next Generation Computing CTNGC 2012, pp 35–39
Tuba M, Subotic M, Stanarevic N (2011) Modified cuckoo search algorithm for unconstrained optimization problems. In: Proceedings of the 5th European conference on European computing conference, pp 263–268
Vechtomova O, Robertson S, Jones S (2003) Query expansion with long-span collocates. Inf Retrieval 6(2):251–273. https://doi.org/10.1023/A:1023936321956
Wasilewski P (2011) Query Expansion by Semantic Modeling of Information Need. In: proceedings of international Workshop CS and P
Wu H, Li J, Kang Y (2018) Exploring noise control strategies for UMLS–based query expansion in health and biomedical information retrieval. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0836-x
Xu J, Croft WB (1996) Query Expansion using Local and Global Document Analysis. ACM SIGIR conference on research and development in information retrieval, pp 4–11. https://doi.org/10.1145/243199.243202
Yang J, Korfhage R (1994) Query modifications using genetic algorithms in vector space models. International Journal of Expert Systems 7(2):165–191
Zhang C, Yang Y, Du Z, Ma C (2016) Particle swarm optimization algorithm based on ontology model to support cloud computing applications. J Ambient Intell Humaniz Comput 7(5):633–638. https://doi.org/10.1007/s12652-015-0262-2
Acknowledgements
We are very thankful to anonymous reviewers for their valuable suggestions. We are also thankful to Dr. Ashish Saini and Dr. Yogesh Gupta to provide their support to access datasets for experiments.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sharma, D.K., Pamula, R. & Chauhan, D.S. A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system. J Ambient Intell Human Comput 15, 829–848 (2024). https://doi.org/10.1007/s12652-019-01247-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01247-9