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Conference Paper: HAR: Hub, authority and relevance scores in multi-relational data for query search

TitleHAR: Hub, authority and relevance scores in multi-relational data for query search
Authors
Issue Date2012
Citation
Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, 2012, p. 141-152 How to Cite?
AbstractIn this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of objects for hub scores and authority scores. The main con- Tribution of this paper is to (i) propose a framework (HAR) that can compute the hub, authority and relevance scores by solving limiting probabilities arising from multi-relational data, and can incorporate input query vectors to handle query-specific search; (ii) show existence and uniqueness of such limiting probabilities so that they can be used for query search effectively; and (iii) develop an iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probabilities. Extensive experimental results on TREC and DBLP data sets suggest that the proposed method is very effective in obtaining relevant results to the querying inputs. In the comparison, we find that the performance of HAR is better than those of HITS, SALSA and TOPHITS. Copyright © 2012 by the Society for Industrial and Applied Mathematics.
Persistent Identifierhttp://hdl.handle.net/10722/276954

 

DC FieldValueLanguage
dc.contributor.authorLi, Xutao-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorYe, Yunming-
dc.date.accessioned2019-09-18T08:35:09Z-
dc.date.available2019-09-18T08:35:09Z-
dc.date.issued2012-
dc.identifier.citationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, 2012, p. 141-152-
dc.identifier.urihttp://hdl.handle.net/10722/276954-
dc.description.abstractIn this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of objects for hub scores and authority scores. The main con- Tribution of this paper is to (i) propose a framework (HAR) that can compute the hub, authority and relevance scores by solving limiting probabilities arising from multi-relational data, and can incorporate input query vectors to handle query-specific search; (ii) show existence and uniqueness of such limiting probabilities so that they can be used for query search effectively; and (iii) develop an iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probabilities. Extensive experimental results on TREC and DBLP data sets suggest that the proposed method is very effective in obtaining relevant results to the querying inputs. In the comparison, we find that the performance of HAR is better than those of HITS, SALSA and TOPHITS. Copyright © 2012 by the Society for Industrial and Applied Mathematics.-
dc.languageeng-
dc.relation.ispartofProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012-
dc.titleHAR: Hub, authority and relevance scores in multi-relational data for query search-
dc.typeConference_Paper-
dc.description.natureLink_to_OA_fulltext-
dc.identifier.doi10.1137/1.9781611972825.13-
dc.identifier.scopuseid_2-s2.0-84880245586-
dc.identifier.spage141-
dc.identifier.epage152-

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