Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning

TL;DR

This paper introduces Rce-KGQA, a novel model that improves multi-hop KGQA by explicitly leveraging relational chains from questions and implicit relations from the KG, significantly outperforming existing methods.

Contribution

The paper proposes a new model that effectively combines explicit question-derived and implicit KG relations for enhanced multi-hop question answering.

Findings

Rce-KGQA outperforms state-of-the-art models on three benchmarks.

The model effectively captures relational chains from questions and KG.

Ablation studies confirm the model's robustness and effectiveness.

Abstract

Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires reasoning over the multi-hop relational chain preserved in KG to arrive at the right answer. Despite recent successes, the existing works on answering multi-hop complex questions still face the following challenges: i) The absence of an explicit relational chain order reflected in user-question stems from a misunderstanding of a user's intentions. ii) Incorrectly capturing relational types on weak supervision of which dataset lacks intermediate reasoning chain annotations due to expensive labeling cost. iii) Failing to consider implicit relations between the topic entity and the answer implied in structured KG because of limited neighborhoods size constraint in subgraph retrieval-based algorithms.To address these issues in multi-hop KGQA, we propose a novel model herein, namely Relational Chain based Embedded KGQA (Rce-KGQA), which simultaneously utilizes the explicit relational chain revealed in natural language question and the implicit relational chain stored in structured KG. Our extensive empirical study on three open-domain benchmarks proves that our method significantly outperforms the state-of-the-art counterparts like GraftNet, PullNet and EmbedKGQA. Comprehensive ablation experiments also verify the effectiveness of our method on the multi-hop KGQA task. We have made our model's source code available at github: https://github.com/albert-jin/Rce-KGQA.