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In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling. Ontology engineering aims at making explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain. Ontology engineering offers a direction towards solving the inter-operability problems brought about by semantic obstacles, i.e. the obstacles related to the definitions of business terms and software classes. Ontology engineering is a set of tasks related to the development of ontologies for a particular domain. Automated processing of information not interpretable by software agents can be improved by adding rich semantics to the corresponding resources, such as video files. One of the approaches for the formal conceptualization of represented knowledge domains is the use of machine-interpretable ontologies, which provide structured data in, or based on, RDF, RDFS, and OWL. Ontology engineering is the design and creation of such ontologies, which can contain more than just the list of terms (controlled vocabulary); they contain terminological, assertional, and relational axioms to define concepts (classes), individuals, and roles (properties) (TBox, ABox, and RBox, respectively).

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Ontology engineering

Modèle de données

En informatique, un modèle de données est un modèle qui décrit la manière dont sont représentées les données dans une organisation métier, un système d'information ou une base de données. Le terme modèle de données peut avoir deux significations : Un modèle de données théorique, c'est-à-dire une description formelle ou un modèle mathématique. Voir aussi modèle de base de données Un modèle de données instance, c'est-à-dire qui applique un modèle de données théorique (modélisation des données) pour créer un modèle de données instance.

Web sémantique

vignette|300px|droite|Logo du W3C pour le Web sémantique Le Web sémantique, ou toile sémantique, est une extension du Web standardisée par le World Wide Web Consortium (W3C). Ces standards encouragent l'utilisation de formats de données et de protocoles d'échange normés sur le Web, en s'appuyant sur le modèle Resource Description Framework (RDF). Le Web sémantique est par certains qualifié de Web 3.0. Selon le W3C, . L'expression a été inventée par Tim Berners-Lee (inventeur du Web et directeur du W3C), qui supervise le développement des technologies communes du Web sémantique.

CS-423: Distributed information systems

This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.

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

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

Modélisation sémantique : données tabulaires et RDF DH-405: Foundations of digital humanities

Introduit la modélisation sémantique par le biais de données tabulaires et de RDF, couvrant les bases de données relationnelles, la migration de schémas, les schémas à l'épreuve du temps, l'interrogation SPARQL et les limitations de métaconnaissance.

Métadonnées Ontologies et Atlas du cerveau MOOC: Neuroscience Reconstructed: Cell Biology

Explore les normes de métadonnées, l'ontologie génétique et les atlas du cerveau dans les neurosciences.

Nexus Forge : créer et utiliser facilement des graphiques de connaissances MOOC: Simulation Neurocience

Explore Nexus Forge, un outil pour créer et utiliser des graphiques de connaissances sans effort.