Ontologie (philosophie)

Résumé

L'ontologie est une branche de la philosophie et plus spécifiquement de la métaphysique qui, dans son sens le plus général, s'interroge sur la signification du mot « être ». est une question considérée comme inaugurale, c'est-à-dire première dans le temps et première dans l'ordre de la connaissance. Elle est celle des premiers penseurs de la Grèce antique, tels Parménide et Platon. Elle déborde très largement le strict cadre de la métaphysique qui, née chez Aristote, étudie les différentes modalités et propriétés de l'être (ne posant déjà plus de problème en soi), avec quoi on a tendance à la confondre. Il faut également distinguer l'ontologie en tant que telle de l'ousiologie, laquelle est la science de l'être entendu en tant qu'essence. Le terme « ontologie », comme celui de métaphysique dans sa signification moderne, n'apparaît que bien plus tard, au début du , où il prend le sens de « philosophie de l'être », mais aussi de « science première ». À suivre Aristote, À partir de

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An ontological approach to representing the product life cycle

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from the Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry.

IOS PRESS2019

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The Industrial Ontologies Foundry Proof-of-Concept Project

The current industrial revolution is said to be driven by the digitization that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology-based information standard may provide benefits not offered by current information standards. Although there have been ontologies developed in the industrial manufacturing domain, they have been fragmented and inconsistent, and little has received a standard status. With successes in developing coherent ontologies in the biological, biomedical, and financial domains, an effort called Industrial Ontologies Foundry (IOF) has been formed to pursue the same goal for the industrial manufacturing domain. However, developing a coherent ontology covering the entire industrial manufacturing domain has been known to be a mountainous challenge because of the multidisciplinary nature of manufacturing. To manage the scope and expectations, the IOF community kicked-off its effort with a proof-of-concept (POC) project. This paper describes the developments within the project. It also provides a brief update on the IOF organizational set up.

SPRINGER-VERLAG BERLIN2018

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Relevant and readily available information is a crucial basis for decision making, problem solving or performing knowledge-intensive work. The motivation for this thesis lays in the fact that in certain situations not all information provided by an application is important and relevant to the end user. Modern enterprise information systems provide huge amounts of information and quite often, in such large volumes of data, users do not know what is important, why something is important and finally, how to find relevant information. Moreover, in complex business environments users may not be fully aware of the current situation which in turn can negatively influence the decision making process. So, it is very important to provide the appropriate information to the user considering the respective situation. However, even if the user is provided with this information, the problem is not essentially solved. The user also has to understand why the provided information is important which means that he/she has to comprehend the current situation or to be aware of the context in which this situation happened. In this way, the user becomes able to fully understand the real meaning of the information. Therefore, it has become crucial for enterprise applications to be aware of the context they are being used in. Nowadays, manufacturing applications collect and store various kinds of data and information. This data describes users as well as the various aspects of business. This means that if properly interpreted, this data could be used to describe the overall user and business context. However, this is no easy task as context data is subject to constant change and can be highly heterogeneous. This work proposes an approach to solve these problems by providing an ontology-based context model and rules for its interpretation. This model will classify with the help of OWL-built ontologies the context of the users (the employees of a business who are accessing the system) and the context of the business. The user and business context will allow for an enterprise application to anticipate which information is important and relevant in order to serve it to the appropriate user. Since the solution utilizes contextual information and provides information and services according to it, the proposed approach could thus be characterized as âcontext-awareâ. In order to correctly model context information, various application domains in which a model could be applied should be taken into account. This basically means that it is necessary to provide a generic model which can be used in various application domains and use-cases. Having a generic model provides the distinct advantages of easy context information exchange as well as reusability of the model. On the other hand, it significantly reduces the expressiveness of the model and requires more computing to interpret it. Therefore, this approach proposes the upper model (ontology) which specifies only the common and generic concepts of context and supports context information exchange. However, it is not possible to model different domains only with the upper model, which means that for each case, a domain specific model should be specified. This domain specific model should extend the generic upper model and describe the specific domain in more detail. [...]

EPFL2014

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