Summary of Knowledge Base Embeddings: Semantics and Theoretical Properties, by Camille Bourgaux et al.
Knowledge Base Embeddings: Semantics and Theoretical Properties
by Camille Bourgaux, Ricardo Guimarães, Raoul Koudijs, Victor Lacerda, Ana Ozaki
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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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 A recent shift in research on knowledge graph embeddings has led to the development of knowledge base embeddings, where models are constrained to incorporate conceptual knowledge. This paper reviews recent methods that embed description logic-based knowledge bases into vector spaces using geometric-based semantics. The authors identify key theoretical properties from the literature and generalize or unify them. They then examine how concrete embedding methods fit within this framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores ways to represent knowledge bases in mathematical spaces, taking into account important concepts. It looks at different methods for doing this and explains how they relate to each other. The goal is to better understand these methods and how they can be used. |
Keywords
» Artificial intelligence » Embedding » Knowledge base » Knowledge graph » Semantics
