GMH: A General Multi-hop Reasoning Model for KG Completion

TL;DR

This paper introduces GMH, a versatile multi-hop reasoning model for knowledge graph completion that effectively handles both short and long-distance reasoning by addressing key decision points with specialized modules.

Contribution

The paper presents a novel general framework with three modules to improve multi-hop reasoning across varied distances in knowledge graphs, filling a gap in existing models.

Findings

Significant improvements over baselines in multiple datasets.

Effective handling of both short and long-distance reasoning.

Enhanced diversity and accuracy in reasoning paths.

Abstract

Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities. To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios. We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop. Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching. The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.