Ontology Embedding: A Survey of Methods, Applications and Resources

TL;DR

This survey comprehensively reviews ontology embedding methods, applications, and resources, highlighting the integration of statistical and machine learning techniques to enhance ontology reasoning and knowledge representation.

Contribution

It systematically categorizes over 80 papers on ontology embedding, introduces a new library mOWL, and discusses future challenges and directions in the field.

Findings

Categorization of ontology embedding methods including geometric, sequence, and graph models

Introduction of the mOWL library for ontology embedding tasks

Discussion of applications in ontology engineering, machine learning, and life sciences

Abstract

Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies can directly support are quite limited in learning, approximation and prediction. One straightforward solution is to integrate statistical analysis and machine learning. To this end, automatically learning vector representation for knowledge of an ontology i.e., ontology embedding has been widely investigated. Numerous papers have been published on ontology embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field. To bridge this gap, we write this survey paper, which first introduces different kinds of semantics of ontologies and formally defines ontology embedding as well as its property of faithfulness. Based on this, it systematically categorizes and analyses a relatively complete set of over 80 papers, according to the ontologies they aim at and their technical solutions including geometric modeling, sequence modeling and graph propagation. This survey also introduces the applications of ontology embedding in ontology engineering, machine learning augmentation and life sciences, presents a new library mOWL and discusses the challenges and future directions.