Towards combinational relation linking over knowledge graphs

Given a knowledge graph and a natural language phrase, relation linking aims to find relations (predicates or properties) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to the more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. father-in-law). In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we define several elementary meta patterns which can be used to build any combinational relation. Then we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. To enhance the system’s understanding ability, we introduce external knowledge during the linking process. Finally, extensive experiments over the real knowledge graph confirm the effectiveness of the proposed method.