Ontology Completion with Graph-Based Machine Learning: A Comprehensive Evaluation

Increasing quantities of semantic resources offer a wealth of human knowledge, but their growth also increases the probability of wrong knowledge base entries. The development of approaches that identify potentially spurious parts of a given knowledge base is therefore highly relevant. We propose an...

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Bibliographic Details
Published in:Machine learning and knowledge extraction 2022-12, Vol.4 (4), p.1107-1123
Main Authors: Mežnar, Sebastian, Bevec, Matej, Lavrač, Nada, Škrlj, Blaž
Format: Article
Language:English
Subjects:
Algorithms
embedding
explainability
Graph representations
Graph theory
Knowledge bases (artificial intelligence)
Knowledge management
Knowledge representation
link prediction
Machine learning
Ontology
ontology completion
Proteins
Semantic web
Semantics
Web Ontology Language-OWL
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Summary:Increasing quantities of semantic resources offer a wealth of human knowledge, but their growth also increases the probability of wrong knowledge base entries. The development of approaches that identify potentially spurious parts of a given knowledge base is therefore highly relevant. We propose an approach for ontology completion that transforms an ontology into a graph and recommends missing edges using structure-only link analysis methods. By systematically evaluating thirteen methods (some for knowledge graphs) on eight different semantic resources, including Gene Ontology, Food Ontology, Marine Ontology, and similar ontologies, we demonstrate that a structure-only link analysis can offer a scalable and computationally efficient ontology completion approach for a subset of analyzed data sets. To the best of our knowledge, this is currently the most extensive systematic study of the applicability of different types of link analysis methods across semantic resources from different domains. It demonstrates that by considering symbolic node embeddings, explanations of the predictions (links) can be obtained, making this branch of methods potentially more valuable than black-box methods.
ISSN:2504-4990
2504-4990
DOI:10.3390/make4040056