Summary of Towards Complex Ontology Alignment Using Large Language Models, by Reihaneh Amini et al.
Towards Complex Ontology Alignment using Large Language Models
by Reihaneh Amini, Sanaz Saki Norouzi, Pascal Hitzler, Reza Amini
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this research paper, the authors aim to leverage Large Language Models (LLMs) to automate the challenging task of complex ontology alignment in the Semantic Web. Traditionally, ontology alignment has focused on identifying simple 1-to-1 relationships through class labels and properties comparison. However, more practically useful explorations of complex alignments remain underexplored, often requiring manual intervention by ontology and domain experts. The authors propose a prompt-based approach that integrates rich ontology content modules to tackle this challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ontology alignment is an important process in the Semantic Web that helps detect relationships between different ontologies. Currently, it’s a hard problem to automate, so it’s usually done manually by experts. Recently, advancements in Large Language Models (LLMs) have opened up new opportunities for enhancing ontology engineering practices. This paper explores how LLMs can be used to make the complex alignment task easier. |
Keywords
» Artificial intelligence » Alignment » Prompt
