Chang-Uk Kwak  Hee-Geun Yoon  Seong-Bae Park

Pseudo Relevance Feedback Query Expansion query suggestion community detection

In order to assist user’s who are in the process of executing a search, a query expansion method suggests keywords that are related to an input query. Recently, several studies have suggested keywords that are identified by finding domains using a clustering method over the documents that are retrieved. However, the clustering method is not relevant when presenting various domains because the number of clusters should be fixed. This paper proposes a method that suggests keywords by finding various domains related to the input queries by using a community detection algorithm. The proposed method extracts words from the top-30 documents of those that are retrieved and builds communities according to the word graph. Then, keywords representing each community are derived, and the represented keywords are used for the query expansion method. In order to evaluate the proposed method, we compared our results to those of two baseline searches performed by the Google search engine and keyword recommendation using TF-IDF in the search results. The results of the evaluation indicate that the proposed method outperforms the baseline with respect to diversity.