Incorporating Subgraph Structure Knowledge Base Question Answering via Neural Reasoning

Abstract

As an effective representation model of the real world knowledge, knowledge base (or knowledge graph) has attracted wide attention from academia and industry. In recent years, with the emergence of large-scale knowledge bases, knowledge base question answering has also attracted attention as a basic application technology of knowledge bases. Among them, the typical method based on semantic parsing transforms questions into answer retrieval on graphs by parsing query sentences, however, which neglects that there are often missing links in knowledge bases. As a result, the above process might fall short in some cases. The typical model based on neural reasoning performs entity similarity ranking by encoding questions, but it cannot solve the cold start problem of given entities in dynamic scenarios. To address the above problems, a neural inference knowledge base question-and-answer method incorporating subgraph structures is proposed to achieve a more adequate inference by taking into account the semantic and structural information of entities in the question-and-answer inference process. Firstly, the question and answer are converted into vectors containing semantic information by the pre-training model RoBERTa. Secondly, the corresponding question and answer subgraphs are constructed based on the entities in the question and answer, and the structural information of the subgraphs is extracted using graph neural networks. Then, the entity representations are pre-trained based on the background knowledge base and fused with the corresponding structural representations. Finally, the candidate answers are rated based on the fused vectors, and the entity with the highest rating is considered as the answer. Extensive experiments are conducted on the WebQuestionsSP dataset, and the experimental results show that the proposed model outperforms other benchmark models.