Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Concept-based interactive query expansion
Proceedings of the 14th ACM international conference on Information and knowledge management
Inferring semantic query relations from collective user behavior
Proceedings of the 17th ACM conference on Information and knowledge management
A survey on session detection methods in query logs and a proposal for future evaluation
Information Sciences: an International Journal
CoSi: context-sensitive keyword query interpretation on RDF databases
Proceedings of the 20th international conference companion on World wide web
Data Mining and Knowledge Discovery
Discovering tasks from search engine query logs
ACM Transactions on Information Systems (TOIS)
On segmentation of eCommerce queries
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A Policy-Based Web Service Redundancy Detection in Wireless Sensor Networks
Journal of Network and Systems Management
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In this work we propose a method that retrieves a list of related queries given an initial input query. The related queries are based on the query log of previously issued queries by human users, which can be discovered using our improved association rule mining model. Users can use the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it exploits only limited query log information and performs relatively better on queries in all frequency divisions.