The Deep Integration of Knowledge Graphs and Large Language Models: Advancements, Challenges, and Future Directions

In recent years, large language models have emerged with amazing capabilities, but they also have limitations such as hallucinations and black boxes, while knowledge graphs have accurate knowledge and symbolic reasoning capabilities. Therefore, the integration of knowledge graph and large language model becomes necessary. The paper systematically conducts the recent research pertaining to the integration of knowledge graph with large language mode. By analyzing the methods and locations of their confluence, we propose a unifying framework that aims to facilitate comprehension and inspiration for fellow researchers in related fields. This framework not only consolidates existing knowledge but also enhances the translational potential of research by delineating innovative pathways for practical implementation.

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Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1109/icsece61636.2024.10729340
Citations
3
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
1.04
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article

The Deep Integration of Knowledge Graphs and Large Language Models: Advancements, Challenges, and Future Directions

Daifeng Li, Fan Xu
3 citations
Advanced Graph Neural Networks
1.04
article

The Deep Integration of Knowledge Graphs and Large Language Models: Advancements, Challenges, and Future Directions

Daifeng Li, Fan Xu
article en
3 citations

Abstract

In recent years, large language models have emerged with amazing capabilities, but they also have limitations such as hallucinations and black boxes, while knowledge graphs have accurate knowledge and symbolic reasoning capabilities. Therefore, the integration of knowledge graph and large language model becomes necessary. The paper systematically conducts the recent research pertaining to the integration of knowledge graph with large language mode. By analyzing the methods and locations of their confluence, we propose a unifying framework that aims to facilitate comprehension and inspiration for fellow researchers in related fields. This framework not only consolidates existing knowledge but also enhances the translational potential of research by delineating innovative pathways for practical implementation.

Sun Yat-sen University (CN)
Quality Education
Openalex Percentile: Top 18%
Advanced Graph Neural Networks
1.04
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The Deep Integration of Knowledge Graphs and Large Language Models: Advancements, Challenges, and Future Directions — Daifeng Li, Fan Xu · (2024) | TGRS Research Map | TGRS