Towards Combinational Relation Linking over Knowledge Graphs

TL;DR

This paper introduces a systematic data-driven approach for combinational relation linking over knowledge graphs, enabling the extraction of subgraph patterns for complex phrases, with enhanced understanding through external knowledge.

Contribution

It proposes a novel relation assembly method guided by meta patterns and incorporates external knowledge to improve combinational relation linking accuracy.

Findings

Effective in extracting subgraph patterns for complex phrases

Improves relation linking accuracy with external knowledge

Validated through extensive experiments on real knowledge graphs

Abstract

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.