A semantic union model for open domain Chinese knowledge base question answering
Abstract In Open-domain Chinese Knowledge Base Question Answering (ODCKBQA), most common simple questions can be answered by a single relational fact in the knowledge base (KB). The abbreviations, aliases, and nesting of entities in Chinese question sentences, and the gap between them and the struct...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39252-w |
| _version_ | 1797769566315610112 |
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| author | Huibin Hao Xiang-e Sun Jian Wei |
| author_facet | Huibin Hao Xiang-e Sun Jian Wei |
| author_sort | Huibin Hao |
| collection | DOAJ |
| description | Abstract In Open-domain Chinese Knowledge Base Question Answering (ODCKBQA), most common simple questions can be answered by a single relational fact in the knowledge base (KB). The abbreviations, aliases, and nesting of entities in Chinese question sentences, and the gap between them and the structured semantics in the knowledge base, make it difficult for the system to accurately return answers. This study proposes a semantic union model (SUM), which concatenates candidate entities and candidate relationships, using a contrastive learning algorithm to learn the semantic vector representation of question and candidate entity-relation pairs, and perform cosine similarity calculations to simultaneously complete entity disambiguation and relation matching tasks. It can provide information for entity disambiguation through the relationships between entities, avoid error propagation, and improve the system performance. The experimental results show that the system achieves a good average F1 of 85.94% on the dataset provided by the NLPCC-ICCPOL 2016 KBQA task. |
| first_indexed | 2024-03-12T21:10:55Z |
| format | Article |
| id | doaj.art-52594491982d4b149570a3192a691495 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T21:10:55Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-52594491982d4b149570a3192a6914952023-07-30T11:12:11ZengNature PortfolioScientific Reports2045-23222023-07-011311910.1038/s41598-023-39252-wA semantic union model for open domain Chinese knowledge base question answeringHuibin Hao0Xiang-e Sun1Jian Wei2School of Electronic Information, Yangtze UniversitySchool of Electronic Information, Yangtze UniversitySchool of Electronic Information, Yangtze UniversityAbstract In Open-domain Chinese Knowledge Base Question Answering (ODCKBQA), most common simple questions can be answered by a single relational fact in the knowledge base (KB). The abbreviations, aliases, and nesting of entities in Chinese question sentences, and the gap between them and the structured semantics in the knowledge base, make it difficult for the system to accurately return answers. This study proposes a semantic union model (SUM), which concatenates candidate entities and candidate relationships, using a contrastive learning algorithm to learn the semantic vector representation of question and candidate entity-relation pairs, and perform cosine similarity calculations to simultaneously complete entity disambiguation and relation matching tasks. It can provide information for entity disambiguation through the relationships between entities, avoid error propagation, and improve the system performance. The experimental results show that the system achieves a good average F1 of 85.94% on the dataset provided by the NLPCC-ICCPOL 2016 KBQA task.https://doi.org/10.1038/s41598-023-39252-w |
| spellingShingle | Huibin Hao Xiang-e Sun Jian Wei A semantic union model for open domain Chinese knowledge base question answering Scientific Reports |
| title | A semantic union model for open domain Chinese knowledge base question answering |
| title_full | A semantic union model for open domain Chinese knowledge base question answering |
| title_fullStr | A semantic union model for open domain Chinese knowledge base question answering |
| title_full_unstemmed | A semantic union model for open domain Chinese knowledge base question answering |
| title_short | A semantic union model for open domain Chinese knowledge base question answering |
| title_sort | semantic union model for open domain chinese knowledge base question answering |
| url | https://doi.org/10.1038/s41598-023-39252-w |
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