Summary of Extract, Define, Canonicalize: An Llm-based Framework For Knowledge Graph Construction, by Bowen Zhang and Harold Soh
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
by Bowen Zhang, Harold Soh
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Automated knowledge graph creation (KGC) from input text is a challenging task. Recent advancements in large language models (LLMs) have led to successful applications of LLMs to KGC, particularly via zero/few-shot prompting. However, these methods face difficulties scaling up to real-world applications due to limitations in handling complex schemas and context windows. To address these issues, we propose the Extract-Define-Canonicalize (EDC) framework, which consists of open information extraction, schema definition, and post-hoc canonicalization. EDC is flexible and can be applied in settings with or without a pre-defined target schema. Additionally, we introduce a trained component that retrieves relevant schema elements from the input text to improve LLMs’ extraction performance. Our experiments on three KGC benchmarks demonstrate that EDC can extract high-quality triplets without parameter tuning and handle larger schemas compared to prior works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want a computer to automatically create a map of relationships between different pieces of information (like people, places, and things). This is called knowledge graph creation. Recent progress in language models has made it possible for these models to help with this task. However, there are still challenges to overcome. For example, the models struggle when dealing with very large or complex maps of relationships. To solve these problems, we developed a new approach that involves three steps: extracting information from text, defining the map’s structure, and making sure everything is correct. Our method can be used in different situations, whether there is already a map to follow or not. We tested our approach on several datasets and found that it works well. |
Keywords
* Artificial intelligence * Few shot * Knowledge graph * Prompting
