A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system

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.

[1]  Zhiguo Gong,et al.  Multi-term Web Query Expansion Using WordNet , 2006, DEXA.

[2]  Ankur Omer,et al.  Erratum to “Next Generation Sequencing: Potential and Application in Drug Discovery” , 2014, The Scientific World Journal.

[3]  S. Valli,et al.  Query Disambiguation Using Clustering and Concept Based Semantic Web Search For efficient Information Retrieval (QDC - CSWS) , 2013 .

[4]  Yogesh Gupta,et al.  A new fuzzy logic based ranking function for efficient Information Retrieval system , 2015, Expert Syst. Appl..

[5]  Aditi Sharan,et al.  Term co-occurrence and context window-based combined approach for query expansion with the semantic notion of terms , 2017, Int. J. Web Sci..

[6]  Iadh Ounis,et al.  A study of parameter tuning for term frequency normalization , 2003, CIKM '03.

[7]  Aditi Sharan,et al.  A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach , 2017, Neural Computing and Applications.

[8]  Guangquan Zhang,et al.  A ${\bm \lambda}$-Cut and Goal-Programming-Based Algorithm for Fuzzy-Linear Multiple-Objective Bilevel Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[9]  W. Bruce Croft,et al.  Quary Expansion Using Local and Global Document Analysis , 1996, SIGIR Forum.

[10]  Iñaki Alegria,et al.  Morphological query expansion and language-filtering words for improving Basque web retrieval , 2013, Lang. Resour. Evaluation.

[11]  A. R. Rivas,et al.  Study of Query Expansion Techniques and Their Application in the Biomedical Information Retrieval , 2014, TheScientificWorldJournal.

[12]  Jorng-Tzong Horng,et al.  Applying genetic algorithms to query optimization in document retrieval , 2000, Inf. Process. Manag..

[13]  Ana Gabriela Maguitman,et al.  A semi-supervised incremental algorithm to automatically formulate topical queries , 2009, Inf. Sci..

[14]  Heiko Schuldt,et al.  Enhancing sketch-based sport video retrieval by suggesting relevant motion paths , 2014, SIGIR.

[15]  Ricardo da Silva Torres,et al.  A multimodal query expansion based on genetic programming for visually-oriented e-commerce applications , 2016, Inf. Process. Manag..

[16]  Shyi-Ming Chen,et al.  Query expansion for document retrieval based on fuzzy rules and user relevance feedback techniques , 2006, Expert Syst. Appl..

[17]  Hatem Haddad,et al.  Towards an effective automatic query expansion process using an association rule mining approach , 2012, Journal of Intelligent Information Systems.

[18]  Byeong Man Kim,et al.  Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[19]  Olivier Curé,et al.  A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest , 2015, BMC Medical Informatics and Decision Making.

[20]  Jawed I. A. Siddiqi,et al.  Adaptive information retrieval system via modelling user behaviour , 2014, J. Ambient Intell. Humaniz. Comput..

[21]  Shyi-Ming Chen,et al.  A new query expansion method for document retrieval based on the inference of fuzzy rules , 2007 .

[22]  M. Tuba,et al.  Modified cuckoo search algorithm for unconstrained optimization problems , 2011 .

[23]  Jianqiang Li,et al.  Exploring noise control strategies for UMLS-based query expansion in health and biomedical information retrieval , 2018 .

[24]  Yongjian Yang,et al.  Particle swarm optimization algorithm based on ontology model to support cloud computing applications , 2016, J. Ambient Intell. Humaniz. Comput..

[25]  Fletcher T. H. Cole,et al.  An alternative approach to natural language query expansion in search engines: Text analysis of non-topical terms in Web documents , 2008, Inf. Process. Manag..

[26]  Robert R. Korfhage,et al.  Query modification using genetic algorithms in vector space models , 1994 .

[27]  Habiba Drias,et al.  Bat Algorithm for Efficient Query Expansion: Application to MEDLINE , 2016, WorldCIST.

[28]  Stephen E. Robertson,et al.  Query Expansion with Long-Span Collocates , 2003, Information Retrieval.

[29]  Jeffrey Xu Yu,et al.  Support IR query refinement by partial keyword set , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[30]  Heung-Seon Oh,et al.  Cluster-based query expansion using external collections in medical information retrieval , 2015, J. Biomed. Informatics.

[31]  Pragati Bhatnagar,et al.  Genetic Algorithm-Based Query Expansion for Improved Information Retrieval , 2015 .

[32]  Hugh E. Williams,et al.  Query expansion using associated queries , 2003, CIKM '03.

[33]  Jie-Sheng Wang,et al.  Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm , 2015, Comput. Intell. Neurosci..

[34]  Ramalingam Gomathi,et al.  A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation , 2014, TheScientificWorldJournal.

[35]  Yong-Hwan Lee,et al.  Improved image retrieval and classification with combined invariant features and color descriptor , 2019, J. Ambient Intell. Humaniz. Comput..

[36]  Mei Tian,et al.  An implicit relevance feedback method for CBIR with real-time eye tracking , 2015, Multimedia Tools and Applications.

[37]  Claudio Carpineto,et al.  A Survey of Automatic Query Expansion in Information Retrieval , 2012, CSUR.

[38]  Shyi-Ming Chen,et al.  A new query reweighting method for document retrieval based on genetic algorithms , 2006, IEEE Transactions on Evolutionary Computation.

[39]  Yogesh Gupta,et al.  A novel Fuzzy-PSO term weighting automatic query expansion approach using combined semantic filtering , 2017, Knowl. Based Syst..

[40]  W. Bruce Croft,et al.  Using Key Concepts in a Translation Model for Retrieval , 2015, SIGIR.

[41]  Peter Willett,et al.  An Upperbound to the Performance of Ranked-output Searching: Optimal Weighting of Query Terms using a Genetic Algorithm , 1996, J. Documentation.

[42]  Vamsidhar Enireddy,et al.  Improved cuckoo search with particle swarm optimization for classification of compressed images , 2015 .

[43]  W. Bruce Croft,et al.  Effective query formulation with multiple information sources , 2012, WSDM '12.

[44]  Songfeng Lu,et al.  Improved salp swarm algorithm based on particle swarm optimization for feature selection , 2018, Journal of Ambient Intelligence and Humanized Computing.

[45]  Devendra K. Tayal,et al.  Intelligent Query Expansion for the Queries including Numerical Terms , 2012 .

[46]  Aditi Sharan,et al.  Rank fusion and semantic genetic notion based automatic query expansion model , 2018, Swarm Evol. Comput..

[47]  Rong Hu,et al.  An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement , 2019, J. Ambient Intell. Humaniz. Comput..

[48]  Stephen E. Robertson,et al.  Relevance weighting of search terms , 1976, J. Am. Soc. Inf. Sci..

[49]  Habiba Drias,et al.  An accelerated PSO for query expansion in web information retrieval: application to medical dataset , 2017, Applied Intelligence.

[50]  James W. Cooper,et al.  OBIWAN-a visual interface for prompted query refinement , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[51]  F. A. Grootjen,et al.  Conceptual query expansion , 2006, Data Knowl. Eng..

[52]  Aditi Sharan,et al.  Relevance Feedback Based Query Expansion Model Using Borda Count and Semantic Similarity Approach , 2015, Comput. Intell. Neurosci..

[53]  Aditi Sharan,et al.  Relevance Feedback-based Query Expansion Model using Ranks Combining and Word2Vec Approach , 2016 .

[54]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[55]  Shyam Lal,et al.  Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images , 2018 .