2011 MultiRankCoRankingforObjectsand

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The 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.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2011 MultiRankCoRankingforObjectsandMichaek Kwok-Po Ng
Xutao Li
Yunming Ye
MultiRank: Co-ranking for Objects and Relations in Multi-relational Data10.1145/2020408.20205942011