Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

TL;DR

This paper introduces a trainable subgraph retriever for knowledge base question answering that improves subgraph selection and overall QA performance, surpassing existing methods.

Contribution

It proposes a decoupled, trainable subgraph retriever that enhances subgraph-based KBQA models and achieves state-of-the-art results.

Findings

SR outperforms existing retrieval methods.

Combined with NSM, achieves new state-of-the-art performance.

Effective weakly supervised pre-training and end-to-end fine-tuning.

Abstract

Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SRl achieves new state-of-the-art performance when combined with NSM, a subgraph-oriented reasoner, for embedding-based KBQA methods.