Summary of Neural-symbolic Reasoning Over Knowledge Graphs: a Survey From a Query Perspective, by Lihui Liu et al.
Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
by Lihui Liu, Zihao Wang, Hanghang Tong
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, Neural Symbolic AI integration combines the strengths of deep learning and symbolic reasoning to develop highly interpretable and explainable AI systems. Knowledge graphs, which store comprehensive human knowledge, pose challenges due to incomplete and noisy data. Traditional symbolic reasoning struggles with these issues, while Neural Symbolic AI offers a promising solution. The rise of large language models (LLMs) enables the extraction and synthesis of knowledge in new ways, further bridging the gap between symbolic and neural methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines traditional symbolic reasoning with the robustness of deep learning to develop highly interpretable and explainable AI systems. It explores the integration of knowledge graph reasoning with large language models, highlighting potential advancements. The survey focuses on query types and classification of neural symbolic reasoning, offering a comprehensive overview for researchers and practitioners in data mining, AI, the Web, and social sciences. |
Keywords
» Artificial intelligence » Classification » Deep learning » Knowledge graph
