BoxLitE: A Faithful Knowledge Base Embedding Based on Convex Optimization
Quick Answer
BoxLitE introduces a knowledge base embedding model leveraging convex optimization for DL-Lite$^{\mathcal{H}}$, enhancing the representation of hierarchies in TBoxes.
Quick Take
BoxLitE introduces a knowledge base embedding model leveraging convex optimization for DL-Lite$^{\mathcal{H}}$, enhancing the representation of hierarchies in TBoxes. It demonstrates that any satisfiable DL-Lite$^{\mathcal{H}}$ knowledge base can achieve weakly faithful embeddings, showcasing a novel approach to KB embedding as a convex optimization problem.
Key Points
- BoxLitE enables convex optimization for knowledge base embeddings in DL-Lite$^{\mathcal{H}}$.
- It maps concepts to convex regions, enhancing hierarchical representation.
- The model guarantees weakly faithful embeddings for satisfiable knowledge bases.
- Formulates KB embedding as a convex optimization problem for better results.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2605. 23937v1 Announce Type: new Abstract: Knowledge base (KB) embeddings aim at combining the capability of classical knowledge graph embeddings to generalize the information present in facts, the ABox, with conceptual knowledge represented in an ontology language, the TBox. Several authors have recently explored the idea of mapping concepts to convex regions in a vector space.
This is useful to represent hierarchies, typically present in TBoxes, since more general concepts can be mapped to larger regions, containing those regions associated with more specific concepts. However, the power of convexity is rarely leveraged during the actual learning tasks. Here, we introduce BoxLitE, a KB embedding model for DL-Lite$^{\mathcal{H}}$ that allows for convex optimization. We show that for any satisfiable DL-Lite$^{\mathcal{H}}$ KB, there is a BoxLitE embedding that is a weakly faithful model.
As a proof of concept, we show how to formulate the KB embedding task as a convex optimization problem and how to obtain embeddings with such desirable faithfulness properties.
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