Summary of Evaluating Class Membership Relations in Knowledge Graphs Using Large Language Models, by Bradley P. Allen and Paul T. Groth
Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models
by Bradley P. Allen, Paul T. Groth
First submitted to arxiv on: 25 Apr 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 new method for evaluating the quality of class membership relations in knowledge graphs, which assign entities to specific classes. The approach uses a zero-shot chain-of-thought classifier that processes descriptions of entities and classes using natural language definitions. The method is evaluated using two publicly available knowledge graphs (Wikidata and CaLiGraph) and 7 large language models. The results show that the gpt-4-0125-preview model achieves high classification performance, with a macro-averaged F1-score of 0.830 on Wikidata data and 0.893 on CaLiGraph data. A manual analysis reveals that most errors are due to issues in the knowledge graphs themselves rather than the evaluation method. This research demonstrates how large language models can assist knowledge engineers in refining knowledge graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make sure class membership relations in knowledge graphs are accurate. It proposes a new way to check these relations by using big language models and natural language definitions of classes. The authors tested this method on two big databases (Wikidata and CaLiGraph) and 7 different language models. The results show that one of the language models, gpt-4-0125-preview, did very well at classifying entities into their correct categories. This means that these language models can help people who build knowledge graphs make them better. |
Keywords
» Artificial intelligence » Classification » F1 score » Gpt » Zero shot
