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

Michael K. Ng; Xutao Li; Yunming Ye

doi:10.1145/2020408.2020594

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

Michael K. Ng^*, Xutao Li, Yunming Ye

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

106 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 language	English
Title of host publication	Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Publisher	Association for Computing Machinery (ACM)
Pages	1217-1225
Number of pages	9
ISBN (Print)	9781450308137
DOIs	https://doi.org/10.1145/2020408.2020594
Publication status	Published - 2011
Event	17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011 - San Diego, CA, United States Duration: 21 Aug 2011 → 24 Aug 2011

Publication series

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

Conference

Conference	17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011
Country/Territory	United States
City	San Diego, CA
Period	21/08/11 → 24/08/11

User-Defined Keywords

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

Access to Document

10.1145/2020408.2020594

Cite this

Ng, M. K., Li, X., & Ye, Y. (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'11 (pp. 1217-1225). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery (ACM). https://doi.org/10.1145/2020408.2020594

Ng, Michael K. ; Li, Xutao ; Ye, Yunming. / MultiRank : Co-ranking for objects and relations in multi-relational data. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11. Association for Computing Machinery (ACM), 2011. pp. 1217-1225 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{0ee3e5419b444d788596b89897637376,

title = "MultiRank: Co-ranking for objects and relations in multi-relational data",

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.",

keywords = "Multi-relational data, Ranking, Rectangular tensors, Stationary probability distribution, Transition probability tensors",

author = "Ng, {Michael K.} and Xutao Li and Yunming Ye",

note = "Copyright: Copyright 2011 Elsevier B.V., All rights reserved.; 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011 ; Conference date: 21-08-2011 Through 24-08-2011",

year = "2011",

doi = "10.1145/2020408.2020594",

language = "English",

isbn = "9781450308137",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery (ACM)",

pages = "1217--1225",

booktitle = "Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11",

address = "United States",

}

Ng, MK, Li, X & Ye, Y 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'11. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery (ACM), pp. 1217-1225, 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, San Diego, CA, United States, 21/08/11. https://doi.org/10.1145/2020408.2020594

MultiRank: Co-ranking for objects and relations in multi-relational data. / Ng, Michael K.; Li, Xutao; Ye, Yunming.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11. Association for Computing Machinery (ACM), 2011. p. 1217-1225 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - MultiRank

T2 - 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011

AU - Ng, Michael K.

AU - Li, Xutao

AU - Ye, Yunming

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Multi-relational data

KW - Ranking

KW - Rectangular tensors

KW - Stationary probability distribution

KW - Transition probability tensors

UR - http://www.scopus.com/inward/record.url?scp=80052650975&partnerID=8YFLogxK

U2 - 10.1145/2020408.2020594

DO - 10.1145/2020408.2020594

M3 - Conference proceeding

AN - SCOPUS:80052650975

SN - 9781450308137

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 1217

EP - 1225

BT - Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11

PB - Association for Computing Machinery (ACM)

Y2 - 21 August 2011 through 24 August 2011

ER -

Ng MK, Li X, Ye Y. 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'11. Association for Computing Machinery (ACM). 2011. p. 1217-1225. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/2020408.2020594

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

Abstract

Publication series

Conference

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this