Context-Dependent Representation of Knowledge Graphs

Recently, there is a growing interest of leveraging graph’s structural information for knowledge representation. However, they fail to capture global connectivity patterns in knowledge graphs or depict unique structural properties of various graph context. In this paper, we propose a novel representation framework, Context-dependent Representation of Knowledge Graphs (CRKG), to utilize the diversity of graph’s structural information for knowledge representation. We introduce triplet context to effectively capture semantic information from two types of graph structures around a triple. One is K-degree neighborhoods of a source entity in the target triple, which captures global connectivity patterns of entities. The other is multiple relation paths between the entity pair in the target triple, reflecting rich inference patterns between entities. Considering the unique characteristics of two kinds of triplet context, we design distinct embedding strategies to preserve their connectivity pattern diversities. Experimental results on three challenging datasets show that CRKG has significant improvements compared with baselines on link prediction task.