Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension

TL;DR

This paper introduces IR4KGC, a novel approach combining information retrieval and reading comprehension to improve knowledge graph completion, especially for relations not inferable from existing knowledge, achieving strong experimental results.

Contribution

The paper presents a new model that leverages retrieval and reading comprehension to enhance knowledge graph completion beyond inference-based methods.

Findings

Effective in completing relations not inferable from existing knowledge

Achieves superior results on KGC datasets

Addresses limitations of inference-only models

Abstract

Knowledge graphs, as the cornerstone of many AI applications, usually face serious incompleteness problems. In recent years, there have been many efforts to study automatic knowledge graph completion (KGC), most of which use existing knowledge to infer new knowledge. However, in our experiments, we find that not all relations can be obtained by inference, which constrains the performance of existing models. To alleviate this problem, we propose a new model based on information retrieval and reading comprehension, namely IR4KGC. Specifically, we pre-train a knowledge-based information retrieval module that can retrieve documents related to the triples to be completed. Then, the retrieved documents are handed over to the reading comprehension module to generate the predicted answers. In experiments, we find that our model can well solve relations that cannot be inferred from existing knowledge, and achieve good results on KGC datasets.