MultiRank: co-ranking for objects and relations in multi-relational data
MultiRank: co-ranking for objects and relations in multi-relational data
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DOI：
10.1145/2020408.2020594
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
M. Ng;Xutao Li;Yunming Ye
中科院分区：
文献类型：
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作者：
M. Ng;Xutao Li;Yunming Ye
本文的主要目的是设计一个多关系数据中对象和关系的联合排序方案。它在数据挖掘和信息检索中有许多重要的应用。然而，在文献中，缺乏一个通用的框架来处理多关系数据的联合排名。本文的主要贡献是（i）提出一个框架（ii）证明了这种概率分布的存在性和唯一性，从而可以非常有效地对对象和关系进行联合排序;以及（iii）开发一种有效的迭代算法来求解一组张量（多元多项式）方程以获得这种概率分布。在真实数据上的大量实验表明，该框架能够成功地为对象和关系提供一个联合排名方案。实验结果还表明，我们的算法计算效率高，并且可以有效地识别有趣且可解释的联合排名结果。
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.