Journal
ELECTRONICS
Volume 11, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/electronics11193214
Keywords
information retrieval; BERT; knowledge graph-based question answering
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Funding
- Soft Science Research Program of Zhejiang Province [2021C25051]
- Zhejiang Province Key Research and Development Project [2020C03071, 2021C03145]
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This paper introduces a knowledge graph-based question answering approach and proposes a joint system that efficiently generates and ranks candidate paths for complex questions. A new text-matching method is also introduced to capture the semantic correlation between questions and candidate paths.
Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, information retrieval-based methods infer the correct answer by constructing and ranking candidate paths, which achieve excellent performance in simple questions but struggle to handle complex questions due to rich entity information and diverse relations. In this paper, we construct a joint system with three subsystems based on the information retrieval methods, where candidate paths can be efficiently generated and ranked, and a new text-matching method is introduced to capture the semantic correlation between questions and candidate paths. Results of the experiment conducted on the China Conference on Knowledge Graph and Semantic Computing 2019 Chinese Knowledge Base Question Answering dataset verify the superiority and efficiency of our approach.
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