TransBox: EL++-closed Ontology Embedding

TL;DR

TransBox introduces a novel ontology embedding method capable of representing complex DL expressions, significantly improving reasoning tasks involving intricate axioms in OWL ontologies.

Contribution

We propose EL++-closed ontology embeddings and develop TransBox, a method that effectively models complex concepts and relations, advancing ontology reasoning capabilities.

Findings

TransBox achieves state-of-the-art performance on real-world datasets.

It effectively models complex axioms in OWL ontologies.

Enhances reasoning tasks like ontology learning and query answering.

Abstract

OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains such as healthcare and bioinformatics. Inspired by the success of knowledge graph embeddings, embedding OWL ontologies has gained significant attention in recent years. Current methods primarily focus on learning embeddings for atomic concepts and roles, enabling the evaluation based on normalized axioms through specially designed score functions. However, they often neglect the embedding of complex concepts, making it difficult to infer with more intricate axioms. This limitation reduces their effectiveness in advanced reasoning tasks, such as Ontology Learning and ontology-mediated Query Answering. In this paper, we propose EL++-closed ontology embeddings which are able to represent any logical expressions in DL via composition. Furthermore, we develop TransBox, an effective EL++-closed ontology embedding method that can handle many-to-one, one-to-many and many-to-many relations. Our extensive experiments demonstrate that TransBox often achieves state-of-the-art performance across various real-world datasets for predicting complex axioms.