Creating or using various knowledge representation models and notations

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Author(s)
Martin, Philippe
Benard, Jeremy
Griffith University Author(s)
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Marimon, F

MasMachuca, M

BerbegalMirabent, J

Bastida, R

Date
2017
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Int Univ Catalonia, Barcelona, SPAIN

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Abstract

There are many knowledge representation (KR) languages (KRLs), i.e., many KRL notations and KRL abstract structure models. They suit different needs. E.g., knowledge modeling and sharing require expressive and concise KRLs to support and ease the entering of precise knowledge. Many KRLs are more suited to knowledge exploitation with computational tractability constraints. Current KR-based tools – including KR translators – allow the use of only one or few KRLs, and hardly allow their end-users to adapt these KRLs to their needs, e.g., the need to exploit even ad hoc KRs. Indeed, some systematic ad hoc usages can be automatically interpreted. Finally, it is difficult to compare KRLs and KRs according to criteria or KRL related best practices. The approach presented in this article addresses these problems by answering an original research question: “can KR import or export methods be specified in a generic way and, if so, how can they and their resources be specified?”. The approach is based on an ontology of KRLs, hence on KRs about KRLs. It is here named KRLO. It has three original features: i) it represents very different KRL abstract models in a uniform way, ii) it represents KRL notations, and iii) it specifies methods for importing and exporting KRs, and hence also translating them. This article presents principles and uses for this approach. We have built Javascript functions and tools that import and export KRs by exploiting KRLO and a parser generator. For these tools to use new KRLs or KRL presentations, their end-users can add or adapt specifications in KRLO. Other tools can use these tools or functions as Web services or modules. No translator between each pair of KRLs needs to be written. At least for export purposes, KRLO can also be exploited via inference engines for OWL2 or Datalog, or via simple path retrieval mechanisms, e.g., via SPARQL queries.

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PROCEEDINGS OF THE 18TH EUROPEAN CONFERENCE ON KNOWLEDGE MANAGEMENT (ECKM 2017), VOLS 1 AND 2

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© The Author(s) 2017. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the author(s).

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Information modelling, management and ontologies

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