Towards Combinational Relation Linking over Knowledge Graphs
This addresses a more general and challenging task in knowledge graph applications such as question answering, but it is incremental as it builds on existing relation linking methods.
The paper tackles the problem of combinational relation linking over knowledge graphs, which extracts subgraph patterns for compound phrases like 'mother-in-law', and proposes a data-driven method with meta patterns and external knowledge, achieving performance improvements as shown in experiments.
Given a natural language phrase, relation linking aims to find a relation (predicate or property) 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 a more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. mother-in-law). In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. We also introduce external knowledge to enhance the system understanding ability. Finally, we conduct extensive experiments over the real knowledge graph to study the performance of the proposed method.