An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering
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 a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/19/3214 |
| _version_ | 1797479721704882176 |
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| author | Yuliang Xiao Lijuan Zhang Jie Huang Lei Zhang Jian Wan |
| author_facet | Yuliang Xiao Lijuan Zhang Jie Huang Lei Zhang Jian Wan |
| author_sort | Yuliang Xiao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:49:51Z |
| format | Article |
| id | doaj.art-6839abe0f1dd42ca8ba710ad40efdd41 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:49:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6839abe0f1dd42ca8ba710ad40efdd412023-11-23T20:08:16ZengMDPI AGElectronics2079-92922022-10-011119321410.3390/electronics11193214An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question AnsweringYuliang Xiao0Lijuan Zhang1Jie Huang2Lei Zhang3Jian Wan4Department of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaDepartment of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaDepartment of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaDepartment of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaDepartment of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaKnowledge 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.https://www.mdpi.com/2079-9292/11/19/3214information retrievalBERTknowledge graph-based question answering |
| spellingShingle | Yuliang Xiao Lijuan Zhang Jie Huang Lei Zhang Jian Wan An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering Electronics information retrieval BERT knowledge graph-based question answering |
| title | An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering |
| title_full | An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering |
| title_fullStr | An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering |
| title_full_unstemmed | An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering |
| title_short | An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering |
| title_sort | information retrieval based joint system for complex chinese knowledge graph question answering |
| topic | information retrieval BERT knowledge graph-based question answering |
| url | https://www.mdpi.com/2079-9292/11/19/3214 |
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