1. 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 , India ,

2. 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)
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3. 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.
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4. Reformulation Process
Query reformulation involves :
Query Submission
Query Analysis
Relevant data sources identification
Query reformulation
Query execution
Answers and integration
Query result presentation
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5. 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.
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6. 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
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7. 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.
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8. Applying Genetic Algorithm
Each Graph traversal is encoded as chromosome.
Selecting appropriate path based on threshold similarity value.
Crossover and mutation .
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9. 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.
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10. Precision vs Recall
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11. Paper Contributions GA based query reformulation strategy.
Optimization performance analysis among the other nature inspired optimization techniques.
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12. 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.
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13. References
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14. References(cont.)
Jones, R., Rey, B., Madani, O., Greiner, W.: Generating Query Substitutions. In: Proc. 15th Intl. ACM Conf. World Wide Web, pp. 387–396 (2006)
Kraft, R., Zien, J.: Mining Anchor Text for Query Refinement. In: Proc 13th ACM Intl. Conf. World Wide Web, pp. 666–674 (2004)
Yin, Z., Shokouhi, M., Craswell, N.: Query Expansion Using External Evidence. In:  Advances in Information Retrieval. Springer, Heidelberg, pp ,(2009) 
Craswell, N., Szummer, M.: Random Walks on the Click Graph. In: Proc. 30th Annual Intl. ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 239–246 (2007)
Agichtein, E., Brill, E., Dumais, S.: Improving Web Search Ranking by In-corporating User Behaviour Information. In: Proc. 29th ACM SIGIR Intl. Conf. Research and Development in Information Retrieval, pp. 19–26 (2006)
Wang, X., Zhai, C.: Learn from Web Search Logs to Organize Search Results. In: Proc. 30th ACM SIGIR Intl. Conf. Research and Development in Information Retrieval, pp. 87–94 (2007)
Dignum, S., Kruschwitz, U., Fasli, M., Kim, Y., Song, D.: In-corporating Seasonality into Search Suggestions Derived from Intranet Query Logs. In: Proc. IEEE/ACM Intl. Conf. Web Intelligence and Intelligent Agent Technology, pp. 425–430 (2010)
Jones, R., Rey, B., Madani, O., and Greiner, W. Generating query substitutions. In WWW ‘06, pp ,(2006)
Mitra, M., Singhal, A., and Buckley, C.: Improving automatic query expansion. In SIGIR ‘98, pp , (1998)
Jones, R., Rey, B., Madani, O., and Greiner, W.: Generating query substitutions. In WWW ‘06, pp , (2006)
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15. Thank You !
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