Building Domain Ontologies From Engineering Standards

Abstract

The use of engineering standards in virtual engineering and their potential as models for the specification of a given domain's ontology are arguably unexplored. The importance of domain modeling in virtual engineering deals directly with the potential benefits that the semantic technologies may bring, allowing to discover implicit knowledge that can be beneficial for engineers. This work presents a state-of-the-art review of the technologies used in our approach, a successful case study where our methodology was applied, and the description and results of an experiment designed to provide a quantitative validation of our methodology.

Complex ontology management using task models

“…In technical domains standards are more consistent and elaborated documents compared to random texts from those domains. On the other hand, additional domain modeling in process of ontology creation can reveal implicit and initially not reflected in standard knowledge, enhancing the quality of standard [6].…”

“…An important source for domain ontology building can be provided by engineering standards [6]. The large amount of work required during ontology creation, including domain conceptualization, resolving ambiguities in meaning of terms, establishing relevant relations, technical validation, is already done in process of standard creation.…”

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

“…Knowledge-based and intelligent-information approaches are required to make sense of the data and use it for better performance of cyber-physical systems [118]. Furthermore, engineering standards can also be employed for domain modeling in virtual engineering, to discover implicit knowledge that can be beneficial for engineers [119]. By making sense of the copious amounts of data gathered through various means, BDA can perform four types of analytics, i.e., descriptive, inquisitive, predictive, and prescriptive [120].…”

Experiments of Trust Prediction in Social Networks by Artificial Neural Networks

“…Further work will involve testing new approaches to cope with imbalanced datasets, as well as other feature definitions. Also, other knowledge modeling approaches based on experience may be tested on this data such as (Artetxe et al 2013), (Toro, Sanchez et al 2012), (Toro, Vaquero et al 2012), (Zhang et al 2013).…”