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Tapping into knowledge base for concept feedback: Leveraging ConceptNet to improve search results for difficult queries
Abstract Query expansion is an important and commonQuery expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback. Copyright 2012 ACM.do-relevance feedback. Copyright 2012 ACM.
Abstractsub Query expansion is an important and commonQuery expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback. Copyright 2012 ACM.do-relevance feedback. Copyright 2012 ACM.
Bibtextype inproceedings  +
Doi 10.1145/2124295.2124344  +
Has author Kotov A. + , Zhai C.X. +
Has extra keyword Automatic query expansion + , Clickthrough data + , Commonsense knowledge + , ConceptNet + , Document collection + , Graph-based representations + , Knowledge base + , Knowledge basis + , Lexico-semantic + , Local analysis + , Multi-step + , Multiple data + , Pseudo relevance feedback + , Query analysis + , Query expansion + , Retrieval performance + , Search results + , Simulation experiments + , Systematic study + , Unsupervised method + , Web resources + , Web searches + , Wikipedia + , Wordnet + , Data mining + , Information retrieval + , Knowledge based systems + , Ontology + , Semantics + , Websites + , Information management +
Has keyword ConceptNet + , Knowledge bases + , Query analysis + , Query expansion +
Isbn 9781450307475  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 403–412  +
Published in WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining +
Title Tapping into knowledge base for concept feedback: Leveraging ConceptNet to improve search results for difficult queries +
Type conference paper  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 8 November 2014 08:53:53  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 08:53:53  +
DateThis property is a special property in this wiki. 2012  +
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