Ontology Embedding: A Survey of Methods, Applications and Resources
Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho, Robert, Hoehndorf, Yuan He, Ian Horrocks

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…
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Taxonomy
TopicsSemantic Web and Ontologies
MethodsSparse Evolutionary Training · Lib · Ontology
