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Thesis defences

PhD Oral Exam - Cheligeer Cheligeer, Information and Systems Engineering

An EBD-enabled design knowledge acquisition framework

DATE & TIME
Tuesday, August 16, 2022 (all day)
COST

This event is free

ORGANIZATION

School of Graduate Studies

CONTACT

Daniela Ferrer

WHERE

Online

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Having enough knowledge and keeping it up to date not only enables designers to execute the design assignment effectively but also gives them a competitive advantage in the design profession. Knowledge elicitation or knowledge acquisition is a crucial component of system design, particularly for tasks requiring transdisciplinary or multidisciplinary cooperation. In system design, there are three forms of knowledge: design process knowledge, generic knowledge, and domain-specific information. Among these, extracting domain-specific information is exceedingly difficult for designers. This proposal presents three works that attempt to bridge the gap between designers and domain expertise. First, a systematic literature review on data-driven demand elicitation is given using the Environment-based Design (EBD) approach. This review address two research objectives: (i) to investigate the present state of computer-aided requirement knowledge elicitation in the domains of engineering; (ii) to integrate EBD methodology into the conventional literature review framework by providing a well-structured research question generation methodology. The second study describes a data-driven interview transcript analysis strategy that employs EBD environment analysis, unsupervised machine learning, and a range of natural language processing (NLP) approaches to assist designers and qualitative researchers in extracting needs when domain expertise is lacking. The objective of the second research is to propose a transfer-learning method-based qualitative text analysis framework that aid researchers to extract useful knowledge from interview data for healthcare promotion decision making. The third work is an EBD-enabled design lexical knowledge acquisition framework that constructs a semantic network -- RomNet automatically from large collection of abstracts from engineering publications. Applying RomNet can improve the design information retrieval quality and communication between each party involved in a design project.

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