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

Kwok Po NG*, Xutao Li, Yunming Ye

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

78 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Pages1217-1225
Number of pages9
DOIs
Publication statusPublished - 2011
Event17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States
Duration: 21 Aug 201124 Aug 2011

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Country/TerritoryUnited States
CitySan Diego, CA
Period21/08/1124/08/11

Scopus Subject Areas

  • Software
  • Information Systems

User-Defined Keywords

  • Multi-relational data
  • Ranking
  • Rectangular tensors
  • Stationary probability distribution
  • Transition probability tensors

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