Summary of Ontological Relations From Word Embeddings, by Mathieu D’aquin and Emmanuel Nauer
Ontological Relations from Word Embeddings
by Mathieu d’Aquin, Emmanuel Nauer
First submitted to arxiv on: 1 Aug 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 The abstract explores the potential of using word embeddings produced by popular neural models like BERT to connect meanings through ontological relationships, such as subsumption. This could enable large-scale knowledge integration in neural models and have implications for ontology matching, evolution, and integration with web ontologies. The authors test this approach by training a simple feed-forward architecture on top of embeddings from several pre-trained models to predict relations between classes and properties in upper-level ontologies. They demonstrate promising accuracy results, with varying generalization abilities depending on the input data. The study also produces a dataset that can be used to enhance these models for further applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how word meanings can be connected using popular neural models like BERT. It’s like a big puzzle where words are related to each other in a special way. This is important because it could help computers understand and combine information from different sources, like the internet. The researchers tested this idea by building a simple computer program that uses these word meanings to predict relationships between concepts in large databases. They found that their approach worked pretty well, but the results varied depending on the type of data they used. This study also created a special dataset that can be used to make these models even better. |
Keywords
» Artificial intelligence » Bert » Generalization
