Summary of Gs-kgc: a Generative Subgraph-based Framework For Knowledge Graph Completion with Large Language Models, by Rui Yang and Jiahao Zhu and Jianping Man and Hongze Liu and Li Fang and Yi Zhou
GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models
by Rui Yang, Jiahao Zhu, Jianping Man, Hongze Liu, Li Fang, Yi Zhou
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel framework for knowledge graph completion (KGC) called GS-KGC, which leverages subgraph information to aid large language models (LLMs) in generating more accurate answers. The framework uses a QA approach and includes a subgraph partitioning algorithm to generate negatives and neighbors. Negatives encourage LLMs to produce a broader range of answers, while neighbors provide additional contextual insights for reasoning. Experiments on four KGC datasets show GS-KGC’s advantages over existing methods, including a 5.6% increase in Hits@3 on FB15k-237N and a 9.3% increase on ICEWS14. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better complete knowledge graphs by using large language models in a new way. It proposes a special method called GS-KGC that uses subgraph information to help the models make more accurate predictions. The approach involves breaking down big chunks of information into smaller parts and then asking questions to get the right answers. This helps the models think more deeply about what they’re doing and come up with better solutions. |
Keywords
» Artificial intelligence » Knowledge graph
