Expressing formal rules within ontology-based models using SWRL: an application to the nuclear industry

Fortineau, Virginie; Fiorentini, Xenia; Paviot, Thomas; Louis-Sidney, Ludovic; Lamouri, Samir

doi:10.1504/ijplm.2014.065458

Cited by 19 publications

(10 citation statements)

References 33 publications

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“…To avoid flawed solutions that are outside of the range of possible designs, companies create design manuals that collect a bewildering array of design rules. A design rule is a constraining prescriptive statement (Fu et al, 2016) -often an unstructured blend of text and graphical objects (equation, table, overload for a designer (Gruszka and N˛ecka, 2017), documents are the most efficient storage solution. However, today, for various reasons, a document-based approach is not suitable anymore.…”

Section: Contextmentioning

confidence: 99%

“…SQWRL), and software (e.g. Protégé) for structuring and reasoning about domain-specific information such as geometry and topology (Tessier and Wang, 2013;Sanya and Shehab, 2014), feature recognition (Wang and Yu, 2014), generative modelling (Skarka, 2007), connectivistic design reuse (Johansson et al, 2018) or nuclear design rules (Fortineau et al, 2014). Nevertheless, knowledge graphs are extremely time-consuming to be developed and managed.…”

Section: Knowledge Graphmentioning

confidence: 99%

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CACDA: A knowledge graph for a context-aware cognitive design assistant

Huet

Pinquié

Véron

et al. 2021

Computers in Industry

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The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to store design rules in large and unstructured documents. To facilitate the integration of design rules and CAD software, we propose a knowledge graph that structures a large set of design rules in a computable format. The knowledge graph organises entities of design rules (nodes), relationships among design rules (edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT -facilitates the development of the data model, especially the definition of the "design context" concept. The knowledge graph paves the way to a context-aware cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient than the traditional document-based design. On average, participants using an unstructured design rules document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision score. Finally, designers supported by the design assistant spend more time designing than searching for applicable design rules compared to the traditional design approach.

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Section: Contextmentioning

confidence: 99%

Section: Knowledge Graphmentioning

confidence: 99%

CACDA: A knowledge graph for a context-aware cognitive design assistant

Huet

Pinquié

Véron

et al. 2021

Computers in Industry

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“…In addition, rules enable the inference on individuals (the A-Box). The Semantic Web Rules Language (SWRL) is an ontology language that integrates OWL with a rule layer (Fortineau et al 2014). It is the combination of OWL-DL and the Rule Markup Language (RuleML) (Horrocks et al 2004).…”

Section: Reasoning On the A-box With Swrl Rulesmentioning

confidence: 99%

Ontology for cloud manufacturing based Product Lifecycle Management

Talhi

Fortineau²,

Huet

et al. 2017

J Intell Manuf

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The manufacturing environment has become increasingly competitive in the past few years, and product development is getting even more complex. The agility of an information system is a way to manage this complexity, and cloud technologies enable the sharing of tools and information in a new way. In particular, in the field of Product Lifecycle Management, a review of the literature demonstrates that there are technical issues that limit the efficient collaboration between the various stakeholders along the product lifecycle. Cloud manufacturing is a new concept that enables the virtualization of manufacturing resources and capabilities and provides them as a service. Therefore it offers new prospects for usage and collaboration in the extended enterprise, and along the product lifecycle. For instance, it helps to manage variations in production demand, by providing a large set of potential manufacturing resources on demand. However, to share the resources within the cloud manufacturing environment, a unification model of the domain information is required, to which any provider and/or user of cloud manufacturing must conform in order to dialog with the other CM stakeholders. This study provides an ontological model of the cloud manufacturing domain in order to support information exchange between the cloud manufacturing resources. The concepts of the proposed ontology are based on a literature review of models of cloud and models of manufacturing. The detailed ontology is then validated using the OntoClean methodology and within its implementation in an industrial scenario.

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“…The expert systems developed are based on technology similar to that of artificial intelligence and are designed to perform repetitive tasks calling for a complex, rigorous work approach. They continue to be developed in the world of industry using a language such as OWL (W3C, 2012), for example, for the modelling and interpretation of rules (Fortineau et al, 2014).…”

Section: Knowledge Support Tools In Designmentioning

confidence: 99%

Proposal for a process oriented knowledge management system (PKMS)

Sidney

Cheutet

Lamouri

2016

IJPD

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Pr. V. Cheutet obtained in 2006 a PhD degree in the field of Mechanical engineering from both the Grenoble-INP engineering school (France) and the University of Genoa (Italy). From 2007 to 2014, he worked at SUPMECA Paris (engineering school) as associate professor. He became full professor in 2014 at INSA Lyon, in first cycle department. He joined the DISP Laboratory and is currently leader of the "Information Systems Agility" team. Pr. V. Cheutet has published more than 50 papers in international journals and conferences. His main research topics are industrial information systems (especially PLM and ERP), knowledge management, industrial and mechanical engineering management.

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Expressing formal rules within ontology-based models using SWRL: an application to the nuclear industry

Cited by 19 publications

References 33 publications

CACDA: A knowledge graph for a context-aware cognitive design assistant

CACDA: A knowledge graph for a context-aware cognitive design assistant

Ontology for cloud manufacturing based Product Lifecycle Management

Proposal for a process oriented knowledge management system (PKMS)

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