Knowledge Base Question Answering via Semantic Analysis
Knowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the tr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/20/4224 |
| _version_ | 1797574041347817472 |
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| author | Yibo Liu Haisu Zhang Teng Zong Jianping Wu Wei Dai |
| author_facet | Yibo Liu Haisu Zhang Teng Zong Jianping Wu Wei Dai |
| author_sort | Yibo Liu |
| collection | DOAJ |
| description | Knowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the traditional expert system of question and answer, it has the advantage of a large-scale background knowledge base and the traceability and interpretability of the question-answering process. Compared with the current ChatGPT (Chat Generative Pre-trained Transformer) technology, it has advantages in the proprietary segmentation field. Aiming at the problem of the accuracy of existing knowledge question-answering methods being low, this paper studies the method of semantic analysis for knowledge question-answering under the support of a knowledge database, proposes a knowledge question-answering method based on the superposition of multiple neural network models, and conducts experimental verification on the publicly available NLPCC2016KBQA(Knowledge Q&A Tasks in the 2016 Natural Language Processing and Chinese Computing Conference) data set. The experimental results show that the F1 value of this method is higher than that of the baseline model. |
| first_indexed | 2024-03-10T21:17:37Z |
| format | Article |
| id | doaj.art-71fd02e43c20445db37df5f8cd04e70d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:17:37Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-71fd02e43c20445db37df5f8cd04e70d2023-11-19T16:18:36ZengMDPI AGElectronics2079-92922023-10-011220422410.3390/electronics12204224Knowledge Base Question Answering via Semantic AnalysisYibo Liu0Haisu Zhang1Teng Zong2Jianping Wu3Wei Dai4School of Information Communication, National University of Defense Technology, Wuhan 430014, ChinaSchool of Information Communication, National University of Defense Technology, Wuhan 430014, ChinaSchool of Information Communication, National University of Defense Technology, Wuhan 430014, ChinaSchool of Information Communication, National University of Defense Technology, Wuhan 430014, ChinaSchool of Information Communication, National University of Defense Technology, Wuhan 430014, ChinaKnowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the traditional expert system of question and answer, it has the advantage of a large-scale background knowledge base and the traceability and interpretability of the question-answering process. Compared with the current ChatGPT (Chat Generative Pre-trained Transformer) technology, it has advantages in the proprietary segmentation field. Aiming at the problem of the accuracy of existing knowledge question-answering methods being low, this paper studies the method of semantic analysis for knowledge question-answering under the support of a knowledge database, proposes a knowledge question-answering method based on the superposition of multiple neural network models, and conducts experimental verification on the publicly available NLPCC2016KBQA(Knowledge Q&A Tasks in the 2016 Natural Language Processing and Chinese Computing Conference) data set. The experimental results show that the F1 value of this method is higher than that of the baseline model.https://www.mdpi.com/2079-9292/12/20/4224knowledge graphknowledge question answeringsemantic analysisneural network model |
| spellingShingle | Yibo Liu Haisu Zhang Teng Zong Jianping Wu Wei Dai Knowledge Base Question Answering via Semantic Analysis Electronics knowledge graph knowledge question answering semantic analysis neural network model |
| title | Knowledge Base Question Answering via Semantic Analysis |
| title_full | Knowledge Base Question Answering via Semantic Analysis |
| title_fullStr | Knowledge Base Question Answering via Semantic Analysis |
| title_full_unstemmed | Knowledge Base Question Answering via Semantic Analysis |
| title_short | Knowledge Base Question Answering via Semantic Analysis |
| title_sort | knowledge base question answering via semantic analysis |
| topic | knowledge graph knowledge question answering semantic analysis neural network model |
| url | https://www.mdpi.com/2079-9292/12/20/4224 |
| work_keys_str_mv | AT yiboliu knowledgebasequestionansweringviasemanticanalysis AT haisuzhang knowledgebasequestionansweringviasemanticanalysis AT tengzong knowledgebasequestionansweringviasemanticanalysis AT jianpingwu knowledgebasequestionansweringviasemanticanalysis AT weidai knowledgebasequestionansweringviasemanticanalysis |