Research Area:  Big Data

Abstract:

Link prediction over a knowledge graph aims to predict the missing head entities h or tail entities t and missing relations r for a triple (h,r,t) . Recent years have witnessed great advance of knowledge graph embedding based link prediction methods, which represent entities and relations as elements of a continuous vector space. Most methods learn the embedding vectors by optimizing a margin-based loss function, where the margin is used to separate negative and positive triples in the loss function. The loss function utilizes the general structures of knowledge graphs, e.g., the vector of r is the translation of the vector of h and t , and the vector of t should be the nearest neighbor of the vector of h+r . However, there are many particular structures, and can be employed to promote the performance of link prediction. One typical structure in knowledge graphs is hierarchical structure, which existing methods have much unexplored. We argue that the hierarchical structures also contain rich inference patterns, and can further enhance the link prediction performance. In this paper, we propose a hierarchy-constrained link prediction method, called hTransM, on the basis of the translation-based knowledge graph embedding methods.

Keywords:  

Author(s) Name:  Manling Li; Denghui Zhang; Yantao Jia; Yuanzhuo none Wang and Xueqi Cheng

Publisher name:  IEEE

DOI:  10.1109/TBDATA.2018.2867583

Volume Information:  Page(s): 1 - 1

Paper Link:   https://ieeexplore.ieee.org/document/8450054