Ontology Engineering Methodologies

Sure, York; Tempich, Christoph; Vrandečić, Denny

doi:10.1002/047003033x.ch9

Cited by 25 publications

(4 citation statements)

References 18 publications

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“…Our findings build nevertheless upon the results on these previous works, some of the most important of which we will mention in this section. Ontology engineering has been subject of several analytical surveys, which assessed ontology engineering methodologies from the theoretical perspective and identifies open issues in the field from its early days (Grüninger & Fox, 1995;Jones, Bench-Capon, & Visser, 1998;Lenat, 1995;Noy & Hafner, 1997;Peterson, Andersen, & Engel, 1998) to the time that preceded the advent of Linked Data (Cardoso, 2007;Corcho, Fernández-Lopéz, & Gómez-Pérez, 2003;Hepp, 2007;Paslaru-Bontas & Tempich, 2006;Simperl, Mochol, & Bürger 2010;Sure, Tempich, & Vrandecic, 2006). The most recent of these studies attested the maturity of ontology engineering as an established engineering discipline, with methodologies used in projects whose success critically depends on a systematic operation of the engineering process, and a constantly improving tool landscape facilitating the involvement of lay users at various stages of the ontology life cycle.…”

Section: Related Workmentioning

confidence: 99%

The Role of Ontology Engineering in Linked Data Publishing and Management

Luczak-Rösch

Simperl

Stadtmüller

et al. 2014

International Journal on Semantic Web and Information Systems

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In this article the authors evaluate the adoption and applicability of established ontology engineering results by the Linked Data providers' community. The evaluation relies on a combination of qualitative and quantitative methods; in particular, the authors conducted an analytical survey containing structured interviews with data publishers in order to give an account of the current ontology engineering practice in Linked Data provisioning, and compared and expanded our findings with statistics on ontology development and usage provided by the Billion Triple Challenges datasets from 2012 (using the vocab.cc platform) and from 2014 and other related tools. The findings of the evaluation allow data practitioners and ontologists to yield a better understanding of the conceptual part of the LOD Cloud; and form the basis for the definition of purposeful, empirically grounded guidelines and best practices for developing, managing and using ontologies in the new application scenarios that arise in the context of Linked Data.

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Section: Related Workmentioning

confidence: 99%

The Role of Ontology Engineering in Linked Data Publishing and Management

Luczak-Rösch

Simperl

Stadtmüller

et al. 2014

International Journal on Semantic Web and Information Systems

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“…Moreover, ontology development is known to be a dynamic process and a literature exists to ensure a consistent ontology evolution (Sure & Tempich, 2004). This property is especially interesting here, since the mechanical experiment domain is still evolving.…”

Section: Ontology For Data Information and Knowledge Modelingmentioning

confidence: 99%

Capitalizing Data, Information and Knowledge on Mechanical Experiments through Ontologies

Cheutet

Leclaire

Botelho

et al. 2013

Product Lifecycle Management for Society

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Abstract. Experiments in a laboratory or a company, either physical or virtual, generate a large amount of data and information (raw data, consolidated results, experimental conditions, etc.) that has to be managed in order to preserve and enhance the knowledge and the expertise of the organization, especially when turnover happens. If simulation data management has been especially explored and standardized (STEP AP 209 for instance), experimental data did not create the same interests and only standard data management tools are proposed. In this paper, we propose an ontology to capitalize such information and knowledge, with an efficient reuse objective. This ontology, for which specific taxonomies have been defined according to the relevant literature of the domain, is illustrated on a set of data on previous experimental campaigns, corresponding to two kinds of mechanical experiments: tribology tests and material characterization tests.

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“…This requires knowledge of engineers, domain experts, analysts, and interviewers, among others. 22 Ontology can serve as the common basis for communication between humans and machines. Marvel et al 23 utilized a flexible ontology for risk assessment during HRC.…”

Section: Introductionmentioning

confidence: 99%

Exploiting visual cues for safe and flexible cyber-physical production systems

Islam

Lughmani

Qureshi

et al. 2019

Advances in Mechanical Engineering

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Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions.

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Ontology Engineering Methodologies

Cited by 25 publications

References 18 publications

The Role of Ontology Engineering in Linked Data Publishing and Management

The Role of Ontology Engineering in Linked Data Publishing and Management

Capitalizing Data, Information and Knowledge on Mechanical Experiments through Ontologies

Exploiting visual cues for safe and flexible cyber-physical production systems

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