%0 Journal Article %1 Franzoni2017 %A Franzoni, Valentina %A Li, Yuanxi %A Leung, Clement H. C. %A Milani, Alfredo %D 2017 %I Springer Verlag %J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) %K Concept and distance,PMING distance,Precision document expansion,Semantic measures,Web qe queryexpansion recall,Query retrieval similarity uncovr %N PART 4 %P 657-672 %R 10.1007/978-3-642-39649-6_47 %T Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion %U https://arxiv.org/abs/1701.05311v1 %V 7974 LNCS %X In this work several semantic approaches to concept-based query expansion and reranking 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 by using statistical results from 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.

@article{Franzoni2017, abstract = {In this work several semantic approaches to concept-based query expansion and reranking 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 by using statistical results from 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.}, added-at = {2021-10-15T08:50:32.000+0200}, author = {Franzoni, Valentina and Li, Yuanxi and Leung, Clement H. C. and Milani, Alfredo}, biburl = {https://www.bibsonomy.org/bibtex/27e73fb0a092b0a981a834963bfddcb4e/simonha94}, doi = {10.1007/978-3-642-39649-6_47}, interhash = {6abac0b0d511a041e3f092b99e34df93}, intrahash = {7e73fb0a092b0a981a834963bfddcb4e}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, keywords = {Concept and distance,PMING distance,Precision document expansion,Semantic measures,Web qe queryexpansion recall,Query retrieval similarity uncovr}, month = {1}, number = {PART 4}, pages = {657-672}, publisher = {Springer Verlag}, timestamp = {2021-10-15T08:52:26.000+0200}, title = {Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion}, url = {https://arxiv.org/abs/1701.05311v1}, volume = {7974 LNCS}, year = 2017 }