Knowledge Graph Embedding with Triple Context

Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph completion tasks. Most existing methods are based on the assumption that a knowledge graph is a set of separate triples, ignoring rich graph features, i.e., structural information in the graph. In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context. We then propose a Triple-Context-based knowledge Embedding model (TCE). For each triple, two kinds of structure information are considered as its context in the graph; one is the outgoing relations and neighboring entities of an entity and the other is relation paths between a pair of entities, both of which reflect various aspects of the triple. Triples along with their contexts are represented in a unified framework, in which way structural information in triple contexts can be embodied. The experimental results show that our model outperforms the state-of-the-art methods for link prediction.