Efficient Algorithm for Web Search Query Reformulation Using Genetic Algorithm A Paper Presentation Vikram Singh Dept. of Computer Engineering , National Institute of Technology, Kurukshetra, Haryana-136119, India Email : vishalsheokand007@gmail.com , viks@nitkkr.ac.in

Introduction Query Reformulation Types of Query Reformulation Process of altering a given query to improve search or retrieval performance. Types of Query Reformulation Query log Analysis Genetic Algorithm Approach Other nature based optimization techniques (ACO) ICCIDM,2015

Query Reformulation Query answering in web based search engine is critical and key issue in dynamic computing. Query reformulation is interactive and iterative. About 28% queries are generalization of previously submitted query of web searches. About 52% of web users reformulate initial queries with anticipation of better results. ICCIDM, 2015

Reformulation Process Query reformulation involves : Query Submission Query Analysis Relevant data sources identification Query reformulation Query execution Answers and integration Query result presentation ICCIDM, 2015

Genetic Algorithm Genetic Algorithm Query is considered as individual (chromosome for GA) Generates optimal path on term association graph Reformulation based on terms in optimal path. ICCIDM, 2015

GA based Query Reformulation Search query q Retrieve top k documents Loading query keywords into Ternary search tree Construction of term graph v Query suggestion Extract keywords from top-k paths Select best path Implement GA ICCIDM, 2015

Associated Term Graph Every Keyword of Ternary search tree has an associated term Graph. Node of graph is the document which contains the keyword. Edges between nodes are based on similarity value. ICCIDM, 2015

Applying Genetic Algorithm Each Graph traversal is encoded as chromosome. Selecting appropriate path based on threshold similarity value. Crossover and mutation . ICCIDM, 2015

Query Reformulation GA converges with set of optimal paths. A path with higher similarity value is preferred. Distinct words are extracted from optimal paths for query suggestion. ICCIDM, 2015

Precision vs Recall ICCIDM, 2015

Paper Contributions GA based query reformulation strategy. Optimization performance analysis among the other nature inspired optimization techniques. ICCIDM, 2015

Conclusion Genetic Algorithm based query reformulation. Build upon Ternary search tree and term graph extracted from documents of initial user query. Optimal plans are selected by GA based approach according to similarity values. Optimization performance of various approach is also discussed.(ACO and PSO) GA emerges as competitively effective on the query reformulation. ICCIDM, 2015

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Thank You ! ICCIDM, 2015