Ontology-Based Representation of Design Decision Hierarchies

Abstract

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Design Ontology Supporting Model-Based Systems Engineering Formalisms

“…For example, MBSE ontologies have been developed to formalize domain-specific concepts and their interrelationships using different languages [17], [18]. Some researchers have provided ontology-based approaches to facilitate the design automation for complex systems [19]. In addition, ontology and formalisms are developed for systems engineering.…”

“…They are expected to provide potential solutions for combining systems engineering approaches and AI technologies. Some researchers have provided an ontologybased approach facilitating the design automation for complex systems [19], [25]. Hao et al [26] proposed an ontology-based method to support knowledge management.…”

The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms

“…To overcome these limits, future cyber-infrastructures should provide advanced digital libraries not only for storing and accessing data but also the different machine learning tools capable of generating appropriate algorithms for each specific application context (Figure 3). These infrastructures should be based on high-level semantic layers (e.g., thesauruses and ontologies) for helping non expert users select the most appropriate computing approaches [101][102][103]. Infrastructures should also provide powerful hardware capabilities (e.g., computing clouds) for executing complex computational tasks (e.g., the training of neural networks and the learning of evolutionary-based algorithms) and semantic annotation tools for constructing ground-truth datasets [104,105] to be used as example datasets for the supervised machine learning approaches.…”

A Reusable and Executable Information Model of Experiments on Human Decision Making in Systems Engineering and Design

“…Although research on ontology is rooted in computer science, ontology is domain neutral and widely applied for knowledge modeling and knowledge reuse. It is exploited in SE&D with many purposes, e.g., supporting design for additive manufacturing (DFAM) [34], supporting manufacturing decision making [35], representing prediction decision tree in manufacturing networks [36], representing design decision hierarchies [37], supporting decision-making in new product development [38], and supporting systematic design space exploration [39]. Notwithstanding, for supporting experimental design in SE&D there is little published work on knowledge reuse or ontology developing.…”