Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Faithful embeddings for EL++ knowledge bases

Xiong, Bo, Potyka, Nico, Tran, Trung-Kien, Nayyeri, Mojtaba and Staab, Steffen 2022. Faithful embeddings for EL++ knowledge bases. Presented at: International Semantic Web Conference (ISWC 2022), Hangzhou, China, 23-27 October 2022. The Semantic Web – ISWC 2022. Lecture Notes in Computer Science , vol.13489 (13489) Springer Cham, pp. 22-38. 10.1007/978-3-031-19433-7_2

Full text not available from this repository.

Abstract

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–protein-protein prediction show that BoxEL outperforms traditional knowledge graph embedding methods as well as state-of-the-art EL++ embedding approaches.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Cham
ISBN: 978-3-031-19432-0
Last Modified: 13 Jun 2023 13:31
URI: https://orca.cardiff.ac.uk/id/eprint/160220

Actions (repository staff only)

Edit Item Edit Item