TY - GEN
T1 - Collective evolutionary concept distance based query expansion for effective web document retrieval
AU - Leung, Clement H.C.
AU - Li, Yuanxi
AU - Milani, Alfredo
AU - Franzoni, Valentina
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Concept distance
KW - PMING distance
KW - Precision and recall
KW - Query expansion
KW - Semantic similarity measures
KW - Web document retrieval
UR - http://www.scopus.com/inward/record.url?scp=84880711690&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39649-6_47
DO - 10.1007/978-3-642-39649-6_47
M3 - Conference proceeding
AN - SCOPUS:84880711690
SN - 9783642396489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 657
EP - 672
BT - Computational Science and Its Applications, ICCSA 2013 - 13th International Conference, Proceedings
PB - Springer Verlag
T2 - 13th International Conference on Computational Science and Its Applications, ICCSA 2013
Y2 - 24 June 2013 through 27 June 2013
ER -