Mining related queries from search engine query logs

ABSTRACT

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.

REFERENCES

• 1: Bruno M. Fonseca , Paulo Golgher , Bruno Pôssas , Berthier Ribeiro-Neto , Nivio Ziviani, Concept-based interactive query expansion, Proceedings of the 14th ACM international conference on Information and knowledge management, October 31-November 05, 2005, Bremen, Germany [doi>10.1145/1099554.1099726]
• 2: Steve Chien , Nicole Immorlica, Semantic similarity between search engine queries using temporal correlation, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060752]
• 3: M. Gilleland. Levenshtein Distance, in Three Flavors. URL: http://www.merriampark.com/ld.htm.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Web-based services

General Terms:
Algorithms, Experimentation

Keywords:
association rule, edit distance, query log, related query, web searching