Summary of A Prompt-based Knowledge Graph Foundation Model For Universal In-context Reasoning, by Yuanning Cui and Zequn Sun and Wei Hu
A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
by Yuanning Cui, Zequn Sun, Wei Hu
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The proposed KG-ICL model achieves a universal reasoning ability by developing a prompt-based foundation for extensive knowledge graphs. The model utilizes in-context learning to generalize and transfer knowledge across diverse graph structures and reasoning settings. A prompt graph is centered with a query-related example fact, which serves as context to understand the query relation. To enable encoding of unseen entities and relations, a unified tokenizer maps entities and relations to predefined tokens. Two message passing neural networks are employed for prompt encoding and KG reasoning. The model outperforms baselines on most datasets in transductive and inductive settings, demonstrating its generalization and universal reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of using knowledge graphs to reason about information. They want to make it so that the same AI system can understand different types of knowledge graphs and use them to answer questions. To do this, they create a special kind of graph called a prompt graph that helps the AI understand what question is being asked. Then, they use a special way of processing words to help the AI understand new information. They tested their approach on many different knowledge graphs and found that it worked well. |
Keywords
» Artificial intelligence » Generalization » Prompt » Tokenizer
