Summary of In-context Learning with Topological Information For Knowledge Graph Completion, by Udari Madhushani Sehwag et al.
In-Context Learning with Topological Information for Knowledge Graph Completion
by Udari Madhushani Sehwag, Kassiani Papasotiriou, Jared Vann, Sumitra Ganesh
First submitted to arxiv on: 11 Dec 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 explores the application of large language models (LLMs) in knowledge graph completion (KGC). Knowledge graphs are crucial for representing structured information, but their effectiveness is often hindered by incompleteness. Recent advances in LLMs have introduced new opportunities for innovation, particularly through in-context learning. The authors develop a novel method that incorporates topological information and ontological knowledge into the context of LLMs to enhance KGC performance. They demonstrate strong performance in both transductive and inductive settings on ILPC-small and ILPC-large datasets, outperforming baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use special language models to help us complete knowledge graphs. Knowledge graphs are like big databases that help us find information, but they’re often incomplete. Recently, new types of language models have come along that might be able to help fill in the gaps. The researchers came up with a new way to use these models that combines their knowledge of words and sentences with special information about how the graph is structured. They tested this method on some big datasets and it did really well compared to other methods. |
Keywords
» Artificial intelligence » Knowledge graph
