Department of Computer Science
Collective evolutionary concept distance based query expansion for effective web document retrieval
In this work several semantic approaches to concept-based query expansion and re-ranking schemes are studied and compared with different ontology-based expansion methods in web document search and retrieval. In particular, we focus on concept-based query expansion schemes where, in order to effectively increase the precision of web document retrieval and to decrease the users' browsing time, the main goal is to quickly provide users with the most suitable query expansion. Two key tasks for query expansion in web document retrieval are to find the expansion candidates, as the closest concepts in web document domain, and to rank the expanded queries properly. The approach we propose aims at improving the expansion phase for better web document retrieval and precision. The basic idea is to measure the distance between candidate concepts using the PMING distance, a collaborative semantic proximity measure, i.e. a measure which can be computed using statistical results from a web search engine. Experiments show that the proposed technique can provide users with more satisfying expansion results and improve the quality of web document retrieval. © 2013 Springer-Verlag Berlin Heidelberg.
Concept distance, PMING distance, Precision and recall, Query expansion, Semantic similarity measures, Web document retrieval
Source Publication Title
Computational Science and Its Applications – ICCSA 2013: 13th International Conference, Ho Chi Minh City, Vietnam, June 24-27, 2013, Proceedings, Part IV
Ho Chi Minh City, Vietnam
Leung, Clement H.C., Yuanxi Li, Alfredo Milani, and Valentina Franzoni. "Collective evolutionary concept distance based query expansion for effective web document retrieval." Computational Science and Its Applications – ICCSA 2013: 13th International Conference, Ho Chi Minh City, Vietnam, June 24-27, 2013, Proceedings, Part IV (2013): 657-672.