Mdωπ: A Modelling Language to Build a Formal Ontology in Either Description Logics or Conceptual Graphs

Nobécourt, Jérôme; Biebow, Brigitte

doi:10.1007/3-540-39967-4_5

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…Moreover, CGs can be translated into other knowledge representation formalisms, such as semantic web languages [81] or Petri nets [82]. Finally, many links exist between CGs and DLs [22,70], which is the most common language in knowledge based applications.…”

Section: Experience-knowledge Formalizationmentioning

confidence: 99%

Generating knowledge in maintenance from Experience Feedback

Ruiz

Foguem

Grabot

2014

Knowledge-Based Systems

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Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, in order to improve the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past activities by (i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); (ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; and (iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes.

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Section: Experience-knowledge Formalizationmentioning

confidence: 99%

Generating knowledge in maintenance from Experience Feedback

Ruiz

Foguem

Grabot

2014

Knowledge-Based Systems

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“…Both formalisms are subsumption-centered and dedicated to the representation of terminological knowledge and assertional knowledge. There are many correspondences between DLs and CGs [6,20]. Ontologies in RDF schemas can also be translated into CGs automatically [5,11].…”

Section: Fkm: Fuzzy Knowledge Managementmentioning

confidence: 99%

Fuzzy Knowledge Management through Knowledge Engineering and Fuzzy Logic

Lai¹,

Huang

Wu³

et al. 2010

JCIT

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Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are used to organize and express various types of fuzzy knowledge through fuzzy CGs. Fuzzy inference rules in fuzzy CGs are identified to offer the deduction capability for reasoning about fuzzy knowledge. Second, fuzzy knowledge models can be classified and stored in a hierarchical ontology system. Ontologies serve as the common understanding of fuzzy knowledge and facilitate the finding of specific fuzzy knowledge relevant to a given domain.

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“…Both formalisms are subsumption-centered and dedicated to the representation of terminological knowledge and assertional knowledge. There are many correspondences between DLs and CGs [7,37]. Ontologies in RDF schemas can also be translated into CGs automatically [6,15].…”

Section: Knowledge Modelingmentioning

confidence: 99%

A knowledge engineering approach to knowledge management

Lai¹

2007

Information Sciences

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Mdωπ: A Modelling Language to Build a Formal Ontology in Either Description Logics or Conceptual Graphs

Cited by 7 publications

References 2 publications

Generating knowledge in maintenance from Experience Feedback

Generating knowledge in maintenance from Experience Feedback

Fuzzy Knowledge Management through Knowledge Engineering and Fuzzy Logic

A knowledge engineering approach to knowledge management

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