Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes

Kestel, Philipp; Kügler, Patricia; Zirngibl, Christoph; Schleich, Benjamin; Wartzack, Sandro

doi:10.1016/j.aei.2019.02.001

Cited by 34 publications

(25 citation statements)

References 20 publications

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“…Therefore, an (semi-) automatization of semantic annotation and the deployment of text mining techniques will be vital for the future application of tribAIn. This usually commences with the accessibility of non-textual documents like PDF, preprocessing of unstructured text, and goes further to section or concept extraction similar to examples in the field of structural mechanical simulations [36]. In this regard, the performance of text mining and automated annotating strongly depends on the accessibility of suitable training data.…”

Section: Discussionmentioning

confidence: 99%

tribAIn—Towards an Explicit Specification of Shared Tribological Understanding

et al. 2020

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Within the domain of tribology, the science and technology for understanding and controlling friction, lubrication, and wear of relatively moving interacting surfaces, countless experiments are carried out and their results are published worldwide. Due to the variety of test procedures and a lack of consistency in the terminology as well as the practice of publishing results in the natural language, accessing and reusing tribological knowledge is time-consuming and experiments are hardly comparable. However, for the selection of potential tribological pairings according to given requirements and to enable comparative evaluations of the behavior of different tribological systems or testing conditions, a shared understanding is essential. Therefore, we present a novel ontology tribAIn (derived from the ancient Greek word “tribein” (= rubbing) and the acronym “AI” (= artificial intelligence)), designed to provide a formal and explicit specification of knowledge in the domain of tribology to enable semantic annotation and the search of experimental setups and results. For generalization, tribAIn is linked to the intermediate-level ontology EXPO (ontology of scientific experiments), supplemented with subject-specific concepts meeting the needs of the domain of tribology. The formalization of tribAIn is expressed in the W3C standard OWL DL. Demonstrating the ability of tribAIn covering tribological experience from experiments, it is applied to a use case with heterogeneous data sources containing natural language texts and tabular data.

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Section: Discussionmentioning

confidence: 99%

tribAIn—Towards an Explicit Specification of Shared Tribological Understanding

et al. 2020

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“…[18] vor. Daneben wird in [19] Text-Mining eingesetzt, um Produktentwickelnden Simulationswissen aus Berechnungsberichten bereitzustellen und sie dadurch zu befähigen valide Simulationen aufzubauen und ihre Gestaltentwürfe abzusichern. Neben der Verarbeitung von Textmengen ist es in der Produktentwicklung beispielsweise auch möglich, mittels deskriptiven Analysemethoden während der Entwicklung generierte Daten aus numerischer oder analytischer Berechnung, Prüfstand-, Feld-oder Fahrversuchen zentral abzubilden und damit zielgerichtet, effizient und ganzheitlich zu analysieren oder Korrelationen aufzudecken [20].…”

Section: Datengetriebene Methoden In Der Produktentwicklungunclassified

Voraussetzungen für den Einsatz datengetriebener Methoden in der Produktentwicklung

Mehlstäubl¹,

Nicklas²,

Gerschütz³

et al. 2021

DS 111: Proceedings of the 32nd Symposium Design for X

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“…OCKER et al [22] use this approach, to link production knowledge in early design phases. Furthermore, consistent requirements engineering processes can be efficiently and purposefully supported by ontologies [23] and tolerancing processes can be automated [24].…”

Section: Semantic Webmentioning

confidence: 99%