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Conference Paper: MultiRank: Co-ranking for objects and relations in multi-relational data

TitleMultiRank: Co-ranking for objects and relations in multi-relational data
Authors
KeywordsStationary probability distribution
Ranking
Multi-relational data
Transition probability tensors
Rectangular tensors
Issue Date2011
Citation
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, p. 1217-1225 How to Cite?
AbstractThe main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results. Copyright 2011 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/276906

 

DC FieldValueLanguage
dc.contributor.authorNg, Michael K.-
dc.contributor.authorLi, Xutao-
dc.contributor.authorYe, Yunming-
dc.date.accessioned2019-09-18T08:35:01Z-
dc.date.available2019-09-18T08:35:01Z-
dc.date.issued2011-
dc.identifier.citationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, p. 1217-1225-
dc.identifier.urihttp://hdl.handle.net/10722/276906-
dc.description.abstractThe main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results. Copyright 2011 ACM.-
dc.languageeng-
dc.relation.ispartofProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-
dc.subjectStationary probability distribution-
dc.subjectRanking-
dc.subjectMulti-relational data-
dc.subjectTransition probability tensors-
dc.subjectRectangular tensors-
dc.titleMultiRank: Co-ranking for objects and relations in multi-relational data-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1145/2020408.2020594-
dc.identifier.scopuseid_2-s2.0-80052650975-
dc.identifier.spage1217-
dc.identifier.epage1225-

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