- (Ng et al., 2011) ⇒ Michaek Kwok-Po Ng, Xutao Li, and Yunming Ye. (2011). “MultiRank: Co-ranking for Objects and Relations in Multi-relational Data.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020594
- Algorithms; data mining; multi-relational data; ranking; rectangular tensors; retrieval models; stationary probability distribution; transition probability tensors
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.
|2011 MultiRankCoRankingforObjectsand||Michaek Kwok-Po Ng|
|MultiRank: Co-ranking for Objects and Relations in Multi-relational Data||10.1145/2020408.2020594||2011|