Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence

TL;DR

This paper introduces DC-Path, a method that enhances knowledge graph reasoning by combining dynamic relation confidence with other indicators to improve path search efficiency and reasoning accuracy.

Contribution

It proposes a novel approach that integrates dynamic relation confidence for better path evaluation and reasoning in knowledge graphs, addressing inefficiencies and sparsity issues.

Findings

Improved path selection accuracy over existing algorithms

Enhanced reasoning performance on knowledge graph tasks

More efficient path search process

Abstract

Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms. The reasoning method based on path features is widely used in the field of knowledge graph reasoning and completion on account of that its have strong interpretability. However, reasoning methods based on path features still have several problems in the following aspects: Path search isinefficient, insufficient paths for sparse tasks and some paths are not helpful for reasoning tasks. In order to solve the above problems, this paper proposes a method called DC-Path that combines dynamic relation confidence and other indicators to evaluate path features, and then guide path search, finally conduct relation reasoning. Experimental result show that compared with the existing relation reasoning algorithm, this method can select the most representative features in the current reasoning task from the knowledge graph and achieve better performance on the current relation reasoning task.