Knowledge Reasoning Based on Neural Tensor Network
Knowledge base (KBs) is a very important part of applications such as Q&A system, but the knowledge base is always faced with incompleteness and the lack of inter-entity relationships. Knowledge reasoning is an important part of the construction of knowledge base, and is intended to find a way t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://doi.org/10.1051/itmconf/20171204004 |
| _version_ | 1818855151980511232 |
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| author | Huang Jian-Hui Huang Jiu-Ming Li Ai-Ping Tong Yong-Zhi |
| author_facet | Huang Jian-Hui Huang Jiu-Ming Li Ai-Ping Tong Yong-Zhi |
| author_sort | Huang Jian-Hui |
| collection | DOAJ |
| description | Knowledge base (KBs) is a very important part of applications such as Q&A system, but the knowledge base is always faced with incompleteness and the lack of inter-entity relationships. Knowledge reasoning is an important part of the construction of knowledge base, and is intended to find a way to supplement these missing relationships. This paper attempts to explore the model complexity of neural tensor network, a very important method of knowledge reasoning, and the reasoning accuracy. By increasing the number of slices in the tensor network layer, the number of parameters to be trained by the model is increased, thereby increasing the complexity of the model. The experimental results show that the number of slices is improved, which is helpful to increase the reasoning accuracy of the model, while the time consumption does not show obvious growth. The accuracy of the model on WordNet and FreeBase increased 2% and 3.2% respectively. |
| first_indexed | 2024-12-19T08:04:03Z |
| format | Article |
| id | doaj.art-9bcaeaf65e1d4efa8c066928b83e9653 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-19T08:04:03Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-9bcaeaf65e1d4efa8c066928b83e96532022-12-21T20:29:47ZengEDP SciencesITM Web of Conferences2271-20972017-01-01120400410.1051/itmconf/20171204004itmconf_ita2017_04004Knowledge Reasoning Based on Neural Tensor NetworkHuang Jian-Hui0Huang Jiu-Ming1Li Ai-Ping2Tong Yong-Zhi3Massive Data Processing Lab National University of Defense TechnologyMassive Data Processing Lab National University of Defense TechnologyMassive Data Processing Lab National University of Defense TechnologyMassive Data Processing Lab National University of Defense TechnologyKnowledge base (KBs) is a very important part of applications such as Q&A system, but the knowledge base is always faced with incompleteness and the lack of inter-entity relationships. Knowledge reasoning is an important part of the construction of knowledge base, and is intended to find a way to supplement these missing relationships. This paper attempts to explore the model complexity of neural tensor network, a very important method of knowledge reasoning, and the reasoning accuracy. By increasing the number of slices in the tensor network layer, the number of parameters to be trained by the model is increased, thereby increasing the complexity of the model. The experimental results show that the number of slices is improved, which is helpful to increase the reasoning accuracy of the model, while the time consumption does not show obvious growth. The accuracy of the model on WordNet and FreeBase increased 2% and 3.2% respectively.https://doi.org/10.1051/itmconf/20171204004 |
| spellingShingle | Huang Jian-Hui Huang Jiu-Ming Li Ai-Ping Tong Yong-Zhi Knowledge Reasoning Based on Neural Tensor Network ITM Web of Conferences |
| title | Knowledge Reasoning Based on Neural Tensor Network |
| title_full | Knowledge Reasoning Based on Neural Tensor Network |
| title_fullStr | Knowledge Reasoning Based on Neural Tensor Network |
| title_full_unstemmed | Knowledge Reasoning Based on Neural Tensor Network |
| title_short | Knowledge Reasoning Based on Neural Tensor Network |
| title_sort | knowledge reasoning based on neural tensor network |
| url | https://doi.org/10.1051/itmconf/20171204004 |
| work_keys_str_mv | AT huangjianhui knowledgereasoningbasedonneuraltensornetwork AT huangjiuming knowledgereasoningbasedonneuraltensornetwork AT liaiping knowledgereasoningbasedonneuraltensornetwork AT tongyongzhi knowledgereasoningbasedonneuraltensornetwork |