Summary of Ontology Population Using Llms, by Sanaz Saki Norouzi et al.
Ontology Population using LLMs
by Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam, Andrew Eells, Pascal Hitzler, Cogan Shimizu
First submitted to arxiv on: 3 Nov 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 Medium Difficulty summary: This research investigates the application of Large Language Models (LLMs) for population knowledge graphs (KGs). KGs integrate data, represent it visually, and are critical for various tasks. However, extracting data from unstructured text can be challenging due to ambiguity and complex interpretations. LLMs excel in natural language understanding and content generation but may “hallucinate,” producing inaccurate outputs. Despite limitations, LLMs offer rapid processing of natural language data and can approximate human-level performance with prompt engineering and fine-tuning. This study evaluates LLM effectiveness for KG population using the Enslaved.org Hub Ontology and reports that LLMs can extract approximately 90% of triples when provided a modular ontology as guidance in prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Scientists are trying to use computers to help organize and understand large amounts of information. This is important because it can be difficult for humans to do this by themselves. One way to do this is by using something called knowledge graphs, which are like maps that connect different pieces of information together. Computers are good at understanding language, but sometimes they make mistakes. The researchers in this study wanted to see if computers could help with the task of putting information into these maps. They found that with some guidance, computers can actually do a pretty good job, and they were able to extract most of the important information. |
Keywords
» Artificial intelligence » Fine tuning » Language understanding » Prompt
