Semantic Modelling and Acquisition of Engineering Knowledge

Sabou, Marta; Kovalenko, Olga; Novák, Petr

doi:10.1007/978-3-319-41490-4_5

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…The last group we identified are cause candidates linked to the information description. For example, it is challenging to combine methods for data integration [7] with domain-specific standards, such as Automa-tionML [7] or ontologies [23].…”

Section: Discussionmentioning

confidence: 99%

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Software Engineering Risks from Technical Debt in the Representation of Product/ion Knowledge

Biffl¹,

Kathrein²,

Lüder³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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In the multidisciplinary production systems engineering (PSE) process, software engineers depend on requirements and design rationales coming from product and production process planning, summarized as product/ion knowledge. Unfortunately, the engineering artifacts coming from product/ion planning often represent important product/ion knowledge incompletely and not well integrated, leading to risks regarding software engineering quality. In this paper, we report on a case study at a large industrial PSE organization, investigating Technical Debt (TD) effects, items, and causes in PSE process documentation and configuration management according to the VDI guideline 3695 Part 2. We focus on requirements for and issues in the representation of product/ion knowledge in the engineering data provided to software engineers. Based on data elicited from PSE domain experts, we model TD concepts based on the Quality Function Deployment method as foundation for TD analysis and risk management. The initial validation with domain experts revealed how software engineers could benefit from improved product/ion knowledge modeling as foundation for better understanding the rationale of engineering design decisions.

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Section: Discussionmentioning

confidence: 99%

“…Sabou et al [23] provide an overview of such ontologies and classify them in terms of the aspects of the PPR process that they cover. For example, OntoCAPE 1 [20] is an ontology for supporting computer-aided process engineering (CAPE) focusing on describing production process information.…”

Section: B Knowledge Representation In Psementioning

confidence: 99%

Software Engineering Risks from Technical Debt in the Representation of Product/ion Knowledge

Biffl¹,

Kathrein²,

Lüder³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

Self Cite

View full text Add to dashboard Cite

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“…An ontology based on a vocabulary of concepts shared by experts has been created through the following process: capturing the experts knowledge about a specific problem regarding the energy efficiency of an urban area and the data needed to model it; creating an informal vocabulary through the terms referred to technical standards, and creating a formal vocabulary according to the Ontology Web Language specifications. The authors of paper [8] employed semantic modeling of engineering data for creating the ontologies capable to extract data for intellectual engineering applications. Whereas in [9], they suggest to apply semantic approach for searching and visualizing academic information.…”

Section: State Of Artmentioning

confidence: 99%

Tools for Strategic Decision Support in Energy Sector Based on Situation Management and Semantic Modeling

Massel

Kuzmin

2018

Proceedings of the VTH International Workshop "Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Clou

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Team, represented by authors, develops a direction, related with support of strategic decision-making in energy sector based on concepts of situation management and semantic modeling. The architecture of instrumental system, called Situational polygon, which is designed for decision-making support is shown. One of the main components of this system is Contingency Management Language or CML (with main focus on management in critical situations conditions in energy). This article deals with approaches to implementation of CML, based on usage of situation calculus and special software complex Semp-TAO.

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“…In the context of this use case signals can be seen as the foundation for common concepts that enable efficient data exchange between engineering disciplines and synchronization of heterogeneous engineering plans. A Virtual Common Data Model (VCDM) has been elicited with industry experts to link individual disciplines [43,44]. Note that key parts of signals (coming from different disciplines) have to be identified, transformed to the VCDM, and mapped to enable a seamless data integration.…”

Section: Common Concepts With Limited Semantic Web Technologies (Levementioning

confidence: 99%

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Winkler¹,

Sabou²,

Biffl³

2017

Quality Control and Assurance - An Ancient Greek Term Re-Mastered

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In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations.

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Semantic Modelling and Acquisition of Engineering Knowledge

Cited by 4 publications

References 34 publications

Software Engineering Risks from Technical Debt in the Representation of Product/ion Knowledge

Software Engineering Risks from Technical Debt in the Representation of Product/ion Knowledge

Tools for Strategic Decision Support in Energy Sector Based on Situation Management and Semantic Modeling

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

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