Summary of Transbox: El++-closed Ontology Embedding, by Hui Yang et al.
TransBox: EL++-closed Ontology Embedding
by Hui Yang, Jiaoyan Chen, Uli Sattler
First submitted to arxiv on: 18 Oct 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 paper, the authors tackle a significant challenge in knowledge representation by proposing a novel approach to embedding OWL ontologies, which are crucial in domains like healthcare and bioinformatics. Current methods focus on learning embeddings for atomic concepts and roles, but neglect the complexity of more intricate axioms. The proposed EL++-closed ontology embeddings can represent any logical expressions in Description Logic via composition, enabling advanced reasoning tasks like Ontology Learning and ontology-mediated Query Answering. The authors also develop TransBox, an effective method that handles many-to-one, one-to-many, and many-to-many relations. Experiments demonstrate state-of-the-art performance on various real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are working to improve how computers understand complex information from the internet. They’re focusing on special types of computer language called OWL ontologies that help explain relationships between different pieces of data. Currently, computers can only learn simple connections, but not more complicated ideas. The authors propose a new way to teach computers about these complex relationships using “embeddings,” which are like shortcuts to understand information quickly. They also develop a special method called TransBox that’s really good at learning many different types of connections between data. By testing this approach on real-world datasets, they show it works much better than existing methods. |
Keywords
» Artificial intelligence » Embedding
