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Ontology completion with graph-based machine learning [Elektronski vir] : a comprehensive evaluationMežnar, Sebastian ...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.Source: Machine learning and knowledge extraction [Elektronski vir]. - ISSN 2504-4990 (Vol. 4, iss. 4, Dec. 2022, str. 1107-1123)Type of material - e-article ; adult, seriousPublish date - 2022Language - englishCOBISS.SI-ID - 132322307
Author
Mežnar, Sebastian |
Bevec, Matej |
Lavrač, Nada |
Škrlj, Blaž
Topics
machine learning |
embedding |
ontology completion |
link prediction |
explainability |
strojno učenje
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Mežnar, Sebastian | 57153 |
Bevec, Matej | |
Lavrač, Nada | 08949 |
Škrlj, Blaž | 52066 |
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