6533b856fe1ef96bd12b3195

RESEARCH PRODUCT

Enterprise Knowledge Modeling, UML vs Ontology: Formal Evaluation

Christophe NicolleMeriem Mejhed MkhininiOuassila Labbani

subject

REPRESENTATIONKnowledge representation and reasoningComputer sciencebusiness.industryIntelligent decision support system02 engineering and technologyOntology (information science)computer.software_genre[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]Knowledge modelingUnified Modeling LanguageCode refactoring020204 information systemsSIMILARITY0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]020201 artificial intelligence & image processingClass diagramSoftware engineeringbusinesscomputercomputer.programming_languageSemantic matching

description

International audience; Everyday activities in enterprises rely heavily on the experts' know-how. Due to experts departure, the loss of expertise and knowledge is a reoccurring problem in these enterprises. Recently, in order to capture experts knowledge into intelligent systems, formal knowledge representation methods, such as ontologies, are being studied and have caught up with non-formal or semi-formal representation, such as UML. The similarities and differences between UML class diagram and computational ontology have for long raised questions about the possibility of synthesizing them in a common representation (usually an ontology). Indeed, the problem of migrating knowledge encoded in UML into an ontology is an active research domain. This paper outlines our approach, which is based on semantic matching between existing ontologies and a UML class diagram, to support UML driven ontology refactoring and engineering.

yearjournalcountryeditionlanguage
2019-09-052019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP)
https://doi.org/10.1109/iccp48234.2019.8959686