Faithful Embeddings for EL++ Knowledge Bases
Bo Xiong,
Nico Potyka,
Trung-Kien Tran,
Mojtaba Nayyeri,
Steffen Staab
Proceedings of the 21st International Semantic Web Conference (ISWC2022),
pp. 1–18,
2022.
Abstract
BibTeX
Project
Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledge (ABox) or suffer from inherent limitations when dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model the logical structure present in the KBs. We present BoxEL, a geometric KB embedding approach that allows for better capturing the logical structure (i.e., ABox and TBox axioms) in the description logic EL++. BoxEL models concepts in a KB as axis-parallel boxes that are suitable for modeling concept intersection, entities as points inside boxes, and relations between concepts/entities as affine transformations. We show theoretical guarantees (soundness) of BoxEL for preserving logical structure. Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB. Experimental results on (plausible) subsumption reasonings and a real-world application for protein-protein prediction show that BoxEL outperforms traditional knowledge graph embedding methods as well as state-of-the-art EL++ embedding
@inproceedings{xiong22_iswc,
title = {Faithful Embeddings for EL++ Knowledge Bases},
author = {Xiong, Bo and Potyka, Nico and Tran, Trung-Kien and Nayyeri, Mojtaba and Staab, Steffen},
year = {2022},
booktitle = {Proceedings of the 21st International Semantic Web Conference (ISWC2022)},
pages = {1--18}
}
