AIERO: An algorithm for identifying engineering relationships in ontologies

Semantic technologies are playing an increasingly popular role as a means for advancing the capabilities of knowledge management systems. Among these advancements, researchers have successfully leveraged semantic technologies, and their accompanying techniques, to improve the representation and search capabilities of knowledge management systems. This paper introduces a further application of semantic techniques. We explore semantic relatedness as a means of facilitating the development of more “intelligent” engineering knowledge management systems. Using semantic relatedness quantifications to analyze and rank concept pairs, this novel approach exploits semantic relationships to help identify key engineering relationships, similar to those leveraged in change management systems, in product development processes. As part of this work, we review several different semantic relatedness techniques, including a meronomic technique recently introduced by the authors. We introduce an aggregate measure, termed “An Algorithm for Identifying Engineering Relationships in Ontologies,” or AIERO, as a means to purposely quantify semantic relationships within product development frameworks. To assess its consistency and accuracy, AIERO is tested using three separate, independently developed ontologies. The results indicate AIERO is capable of returning consistent rankings of concept pairs across varying knowledge frameworks. A PCB (printed circuit board) case study then highlights AIERO’s unique ability to leverage semantic relationships to systematically narrow where engineering interdependencies are likely to be found between various elements of product development processes

Towards Industrial Implementation of Emerging Semantic Technologies

Every new design, project, or procedure within a company generates a considerable amount of new information and important knowledge. Furthermore, a tremendous amount of legacy knowledge already exists in companies in electronic and non-electronic formats, and techniques are needed for representing, structuring and reusing this knowledge. Many researchers have spent considerable time and effort developing semantic knowledge management systems, which in theory are presumed to address these problems. Despite significant research investments, little has been done to implement these systems within an industrial setting. In this paper we identify five main requirements to the development of an industry-ready application of semantic knowledge management systems and discuss how each of these can be addressed. These requirements include the ease of new knowledge management software adoption, the incorporation of legacy information, the ease of use of the user interface, the security of the stored information, and the robustness of the software to support multiple file types and allow for the sharing of information across platforms. Collaboration with Raytheon, a defense and aerospace systems company, allowed our team to develop and demonstrate a successful adoption of semantic abilities by a commercial company. Salient features of this work include a new tool, the e-Design MemoExtractor Software Tool, designed to mine and capture company information, a Raytheon-specific extension to the e-Design Framework, and a novel semantic environment in the form of a customized semantic wikiSMW+. The advantages of this approach are discussed in the context of the industrial case study with Raytheon

Bayesian Evaluation of Engineering Models

ABSTRACT This paper deals with the development of simulation-based design models under uncertainty, and presents an approach for building surrogate models and validating them for their efficacy and relevance from a design decision perspective. Specifically, this work addresses the fundamental research issue of how to build such surrogate models that are computationally efficient and sufficiently accurate, and meaningful from the viewpoint of its subsequent use in design. Towards this goal, this work presents a Bayesian analysis based iterative model building and model validation process leading to reliable and accurate surrogate models, which can then be invoked in the final design optimization phase. The resulting surrogate models can be expected to act as abstractions or idealizations of the engineering analysis models and can mimic system performance in a computationally efficient manner to facilitate design decisions under uncertainty. This is accomplished by first building initial models, and then refining and validating them over many stages, in line with the iterative nature of the engineering design process. Salient features of this work include the introduction of a novel preference-based design screening strategy nested in an optimally-selected prior information set for validation purposes; and the use of a Bayesian evaluation based model-updating technique to capture new information and enhance model's value and effectiveness. A case study of the design of a windshield wiper arm is used to demonstrate the overall methodology and the results are discussed

A Semantic Information Model for Capturing and Communicating Design Decisions

A semantic information model to improve reuse and communication of engineering design knowledge is presented in this paper. We consider design to be a process involving a sequence of decisions informed by the current state of information. As such, the information model developed is structured to reflect the conceptualizations of engineering design decisions with a particular emphasis on semantically capturing design rationale. Through the approach presented, knowledge reuse is achieved by communicating design rationale. A case study is presented to illustrate two key features of the approach: (1) seamless integration of separate modular domain ontologies and instance knowledge related to engineering design that are needed to support decision making and (2) the explicit documentation of design rationale through design decisions