ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs

TL;DR

ReaRev introduces an adaptive reasoning approach for KGQA that iteratively refines instructions and employs GNNs to emulate BFS, significantly improving reasoning accuracy on incomplete KGs and complex questions.

Contribution

The paper presents ReaRev, a novel method that enhances instruction decoding and execution in KGQA through adaptive reasoning and graph neural networks.

Findings

Outperforms previous state-of-the-art on three KGQA benchmarks.

Effective in handling incomplete knowledge graphs.

Improves reasoning for complex questions.

Abstract

Knowledge Graph Question Answering (KGQA) involves retrieving entities as answers from a Knowledge Graph (KG) using natural language queries. The challenge is to learn to reason over question-relevant KG facts that traverse KG entities and lead to the question answers. To facilitate reasoning, the question is decoded into instructions, which are dense question representations used to guide the KG traversals. However, if the derived instructions do not exactly match the underlying KG information, they may lead to reasoning under irrelevant context. Our method, termed ReaRev, introduces a new way to KGQA reasoning with respect to both instruction decoding and execution. To improve instruction decoding, we perform reasoning in an adaptive manner, where KG-aware information is used to iteratively update the initial instructions. To improve instruction execution, we emulate breadth-first search (BFS) with graph neural networks (GNNs). The BFS strategy treats the instructions as a set and allows our method to decide on their execution order on the fly. Experimental results on three KGQA benchmarks demonstrate the ReaRev's effectiveness compared with previous state-of-the-art, especially when the KG is incomplete or when we tackle complex questions. Our code is publicly available at https://github.com/cmavro/ReaRev_KGQA.