A Matrix Approach to Implicit Relationship Finding in Large-scale Knowledge Bases
Relationships between entities in a Knowledge Base (KB) are not always explicitly expressed. In addition, entities may implicitly exist within explicit ones. These phenomena are very common when it comes to large-scale KBs. Finding implicit relationships in a KB can make the original KB more meaningful and enhance its potential in real world applications. In this paper, we focus on the problem of .nding implicitrelationship networks in large-scale KBs. Since a network can be mathematically expressed as a matrix, the process of reasoning for implicit relationship .nding can be transformed to matrix computation. Considering that there are many advantages for matrix computation instead of logic based and graph based reasoning (such as scalability for storage), by realizing the mathematical nature of KBs, we use matrix transformation and computation to investigate the problem of implicit relationship .nding. We give several illustrative real world examples using large-scale KBs to validate this framework. In addition, we also investigate the potential problems of scalability on matrix storage, as well as the cost for computation and time. Based on the proposed approach and the consideration on the scalability issue, we develop the MIRF and MIRF-L algorithms which can ef.ciently process this kind of problem if the rules in concrete cases can be clearly expressed.
Yan Wang Yi Zeng Ning Zhong Zhisheng Huang
International WIC Institute, Beijing University of Technology Beijing, China International WIC Institute, Beijing University of Technology Beijing, China Department of Life Scie Department of Arti.cial Intelligence, Vrije University Amsterdam Amsterdam, the Netherlands
国际会议
The Seventh International Conference on Semantics,Knowledge,and Grids(第七届语义、知识与网格国际会议 SKG 20110)
北京
英文
237-244
2011-10-24（万方平台首次上网日期，不代表论文的发表时间）