Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion
The completeness of knowledge graphs is critical to their effectiveness across various applications. However, existing knowledge graph completion methods face challenges such as difficulty in adapting to new entity information, parameter explosion, and limited generalization capability. To address t...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10766600/ |
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| _version_ | 1841533409100300288 |
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| author | Ying Zhang Yangpeng Shen Gang Xiao Jinghui Peng |
| author_facet | Ying Zhang Yangpeng Shen Gang Xiao Jinghui Peng |
| author_sort | Ying Zhang |
| collection | DOAJ |
| description | The completeness of knowledge graphs is critical to their effectiveness across various applications. However, existing knowledge graph completion methods face challenges such as difficulty in adapting to new entity information, parameter explosion, and limited generalization capability. To address these issues, this paper proposes a knowledge graph completion framework that integrates large language models with case-based reasoning (CBR-LLM). By combining non-parametric reasoning with the semantic understanding capabilities of large language models, the framework not only improves completion accuracy but also significantly enhances generalization under various data-missing scenarios. Experimental results demonstrate that CBR-LLM excels in handling complex reasoning tasks and large-scale data-missing scenarios, providing an efficient and scalable solution for knowledge graph completion. |
| format | Article |
| id | doaj-art-5d6649fbaaf0418d826ac73e45f81bbd |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5d6649fbaaf0418d826ac73e45f81bbd2025-01-16T00:01:56ZengIEEEIEEE Access2169-35362024-01-011217701217702710.1109/ACCESS.2024.350543310766600Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph CompletionYing Zhang0https://orcid.org/0000-0002-1562-2895Yangpeng Shen1Gang Xiao2Jinghui Peng3Academy of Military Sciences (AMS), Institute of Systems Engineering, Beijing, ChinaCITIC Bank, Beijing, ChinaAcademy of Military Sciences (AMS), Institute of Systems Engineering, Beijing, ChinaSchool of Artificial Intelligence, Anhui Polytechnic University, Wuhu, Anhui, ChinaThe completeness of knowledge graphs is critical to their effectiveness across various applications. However, existing knowledge graph completion methods face challenges such as difficulty in adapting to new entity information, parameter explosion, and limited generalization capability. To address these issues, this paper proposes a knowledge graph completion framework that integrates large language models with case-based reasoning (CBR-LLM). By combining non-parametric reasoning with the semantic understanding capabilities of large language models, the framework not only improves completion accuracy but also significantly enhances generalization under various data-missing scenarios. Experimental results demonstrate that CBR-LLM excels in handling complex reasoning tasks and large-scale data-missing scenarios, providing an efficient and scalable solution for knowledge graph completion.https://ieeexplore.ieee.org/document/10766600/Case-based reasoninglarge language modelinformation entropyknowledge graph completion |
| spellingShingle | Ying Zhang Yangpeng Shen Gang Xiao Jinghui Peng Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion IEEE Access Case-based reasoning large language model information entropy knowledge graph completion |
| title | Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion |
| title_full | Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion |
| title_fullStr | Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion |
| title_full_unstemmed | Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion |
| title_short | Leveraging Non-Parametric Reasoning With Large Language Models for Enhanced Knowledge Graph Completion |
| title_sort | leveraging non parametric reasoning with large language models for enhanced knowledge graph completion |
| topic | Case-based reasoning large language model information entropy knowledge graph completion |
| url | https://ieeexplore.ieee.org/document/10766600/ |
| work_keys_str_mv | AT yingzhang leveragingnonparametricreasoningwithlargelanguagemodelsforenhancedknowledgegraphcompletion AT yangpengshen leveragingnonparametricreasoningwithlargelanguagemodelsforenhancedknowledgegraphcompletion AT gangxiao leveragingnonparametricreasoningwithlargelanguagemodelsforenhancedknowledgegraphcompletion AT jinghuipeng leveragingnonparametricreasoningwithlargelanguagemodelsforenhancedknowledgegraphcompletion |