Crisp to fuzzy ontology conversion in the context of social networks A new approach
Autor: | M. H. Fazel Zarandi, Hoda Safaeipour, Susan Bastani |
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Rok vydání: | 2020 |
Předmět: |
Ontology Inference Layer
social networks Computer science Process ontology 02 engineering and technology Ontology (information science) Machine learning computer.software_genre Fuzzy Logic 020204 information systems 0202 electrical engineering electronic engineering information engineering Upper ontology automation business.industry Ontology-based data integration Multi Central Network Suggested Upper Merged Ontology fuzzy ontology crisp ontology ComputingMethodologies_PATTERNRECOGNITION Co-Authorship Network Fuzzy set operations 020201 artificial intelligence & image processing Artificial intelligence ComputingMethodologies_GENERAL business computer Ontology alignment |
Zdroj: | NAFIPS |
DOI: | 10.6084/m9.figshare.12847463 |
Popis: | Fuzzy ontology is a generalization of crisp ontology for modeling uncertain information and has been applied in recent years for supporting different activities of semantic web. However, there are great collections of crisp ontologies developed so far in various domains which are not appropriate for decision making in fuzzy environment. Accordingly, this paper aims at presenting an approach to automatically convert a crisp ontology to fuzzy ontology in the context of social networks. Furthermore, this paper demonstrates that the combination of a learning process of crisp ontology with proposed approach, decreases computational complexity of fuzzy ontology learning due to breaking the task to two optimal steps. Accordingly, the approach allows for an advantageous application of various crisp clustering techniques in fuzzy ontology context. |
Databáze: | OpenAIRE |
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