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

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 both short and long scenarios. We argue that there are two key issues for long distance reasoning: i) which edge to select, and ii) when to stop the search. In this work, 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.

Question-Aware Memory Network for Multi-hop Question Answering in Human-Robot Interaction

“…KG embedding converts symbolic representation of knowledge triples in a KG into continuous semantic spaces by embedding entities and relations into high-dimension vectors [29]. It can effectively improve the downstream tasks such as KG completion [30,31], relation extraction [32] and KBQA [33].…”