Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph

TL;DR

This paper introduces a semantic-structure-aware approach for query graph prediction in knowledge graph question answering, using a novel Structure-BERT to improve candidate filtering and ranking, leading to better accuracy.

Contribution

The paper proposes a new method that incorporates semantic structure prediction with Structure-BERT to enhance query graph generation in KGQA.

Findings

Outperforms state-of-the-art methods on MetaQA and WebQuestionsSP benchmarks.

Effectively filters noisy query graph candidates using semantic structure prediction.

Improves accuracy of question answering over knowledge graphs.

Abstract

Building query graphs from natural language questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctly answered if its query graph is built correctly and the right answer is then retrieved by issuing the query graph against the KG. Therefore, this paper focuses on query graph generation from natural language questions. Existing approaches for query graph generation ignore the semantic structure of a question, resulting in a large number of noisy query graph candidates that undermine prediction accuracies. In this paper, we define six semantic structures from common questions in KGQA and develop a novel Structure-BERT to predict the semantic structure of a question. By doing so, we can first filter out noisy candidate query graphs, and then rank the remaining candidates with a BERT-based ranking model. Extensive experiments on two popular benchmarks MetaQA and WebQuestionsSP (WSP) demonstrate the effectiveness of our method as compared to state-of-the-arts.