The US engineering industry base is facing a significant loss of knowledge due to large numbers of employees retiring in the next decade. Problems in various product developments including product design may arise when the expertise is no longer available or the knowledge is forgotten. Also, most of product design knowledge is not reusable, because product design knowledge in an organization remains un-codified. Generally, knowledge-based system can solve or infer these problems. However, knowledge-based systems have been developed solely through the use of rule-based approach, which allows for easy modeling of expert reasoning, but such an approach is not general and for a specific use; thus, existing experience and analyses show that this approach has serious limitations on associations between observable findings and diagnostic hypotheses. Furthermore, the product development knowledge cannot be appropriately acquired, represented, and reused by these techniques. To address these challenges, this research develops new methodologies and tools to capture, represent, store, and reuse domain knowledge from experts and implement a novel web-based causal product design knowledge management system to systematically utilize the knowledge from experts, who are currently working or retired. The particular emphasis is on these research areas: 1) design knowledge acquisition, 2) causal knowledge representation, 3) causal knowledge evaluation and index, 4) causal knowledge integration, 5) and causal design knowledge management system.
This research aims to extend design, technological and computational methods in knowledge acquisition, knowledge representation, integration of knowledge, web-based knowledge management system to design problem solving processes. Results from this research are expected to advance our understanding of 1) capturing domain knowledge from experts, 2) systematic knowledge acquisition for engineering knowledge retention, 3) capturing and transforming existing procedural engineering knowledge to better knowledge representation formalism, 4) evaluating causal knowledge to make design decision, 5) comparing multiple design knowledge in heterogeneous product, 6) integrating existing design knowledge to generate refined knowledge, 7) and systematic knowledge management using information technologies and tools.
","abstract_html":"<p>The US engineering industry base is facing a significant loss of knowledge due to large numbers of employees retiring in the next decade. Problems in various product developments including product design may arise when the expertise is no longer available or the knowledge is forgotten. Also, most of product design knowledge is not reusable, because product design knowledge in an organization remains un-codified. Generally, knowledge-based system can solve or infer these problems. However, knowledge-based systems have been developed solely through the use of rule-based approach, which allows for easy modeling of expert reasoning, but such an approach is not general and for a specific use; thus, existing experience and analyses show that this approach has serious limitations on associations between observable findings and diagnostic hypotheses. Furthermore, the product development knowledge cannot be appropriately acquired, represented, and reused by these techniques. To address these challenges, this research develops new methodologies and tools to capture, represent, store, and reuse domain knowledge from experts and implement a novel web-based causal product design knowledge management system to systematically utilize the knowledge from experts, who are currently working or retired. The particular emphasis is on these research areas: 1) design knowledge acquisition, 2) causal knowledge representation, 3) causal knowledge evaluation and index, 4) causal knowledge integration, 5) and causal design knowledge management system.</p> <p>This research aims to extend design, technological and computational methods in knowledge acquisition, knowledge representation, integration of knowledge, web-based knowledge management system to design problem solving processes. Results from this research are expected to advance our understanding of 1) capturing domain knowledge from experts, 2) systematic knowledge acquisition for engineering knowledge retention, 3) capturing and transforming existing procedural engineering knowledge to better knowledge representation formalism, 4) evaluating causal knowledge to make design decision, 5) comparing multiple design knowledge in heterogeneous product, 6) integrating existing design knowledge to generate refined knowledge, 7) and systematic knowledge management using information technologies and tools.</p>","abstract_has_math":false,"creators":["Kim, Yun Seon"],"institution":null,"degree_name":"Ph.D.","degree_level":"Open Access Dissertation","degree_discipline":"Industrial and Manufacturing Engineering","degree_department":null,"school":null,"contributors":["Kyoung-Yun Kim"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T05:58:42Z","subjects":["causal knowledge evaluation","causal knowledge evaluation index","causal knowledge integration","causal knowledge management system","causal knowledge representation","knowledge acquisition","Industrial Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wayne.edu/oa_dissertations/135","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kyoung-Yun Kim"]},{"key":"dc:creator","label":"Author","values":["Kim, Yun Seon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2010-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Manufacturing Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["causal knowledge evaluation","causal knowledge evaluation index","causal knowledge integration","causal knowledge management system","causal knowledge representation","knowledge acquisition","Industrial Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wayne.edu/oa_dissertations/135"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The US engineering industry base is facing a significant loss of knowledge due to large numbers of employees retiring in the next decade. Problems in various product developments including product design may arise when the expertise is no longer available or the knowledge is forgotten. Also, most of product design knowledge is not reusable, because product design knowledge in an organization remains un-codified. Generally, knowledge-based system can solve or infer these problems. However, knowledge-based systems have been developed solely through the use of rule-based approach, which allows for easy modeling of expert reasoning, but such an approach is not general and for a specific use; thus, existing experience and analyses show that this approach has serious limitations on associations between observable findings and diagnostic hypotheses. Furthermore, the product development knowledge cannot be appropriately acquired, represented, and reused by these techniques. To address these challenges, this research develops new methodologies and tools to capture, represent, store, and reuse domain knowledge from experts and implement a novel web-based causal product design knowledge management system to systematically utilize the knowledge from experts, who are currently working or retired. The particular emphasis is on these research areas: 1) design knowledge acquisition, 2) causal knowledge representation, 3) causal knowledge evaluation and index, 4) causal knowledge integration, 5) and causal design knowledge management system.
This research aims to extend design, technological and computational methods in knowledge acquisition, knowledge representation, integration of knowledge, web-based knowledge management system to design problem solving processes. Results from this research are expected to advance our understanding of 1) capturing domain knowledge from experts, 2) systematic knowledge acquisition for engineering knowledge retention, 3) capturing and transforming existing procedural engineering knowledge to better knowledge representation formalism, 4) evaluating causal knowledge to make design decision, 5) comparing multiple design knowledge in heterogeneous product, 6) integrating existing design knowledge to generate refined knowledge, 7) and systematic knowledge management using information technologies and tools.
"]},{"key":"dc:title","label":"Title","values":["Causal Product Knowledge Management"]}]}],"canonical_facts":{"dc:contributor":["Kyoung-Yun Kim"],"dc:creator":["Kim, Yun Seon"],"dc:date.available":["2010-01-01T08:00:00Z"],"dc:description.abstract":["The US engineering industry base is facing a significant loss of knowledge due to large numbers of employees retiring in the next decade. Problems in various product developments including product design may arise when the expertise is no longer available or the knowledge is forgotten. Also, most of product design knowledge is not reusable, because product design knowledge in an organization remains un-codified. Generally, knowledge-based system can solve or infer these problems. However, knowledge-based systems have been developed solely through the use of rule-based approach, which allows for easy modeling of expert reasoning, but such an approach is not general and for a specific use; thus, existing experience and analyses show that this approach has serious limitations on associations between observable findings and diagnostic hypotheses. Furthermore, the product development knowledge cannot be appropriately acquired, represented, and reused by these techniques. To address these challenges, this research develops new methodologies and tools to capture, represent, store, and reuse domain knowledge from experts and implement a novel web-based causal product design knowledge management system to systematically utilize the knowledge from experts, who are currently working or retired. The particular emphasis is on these research areas: 1) design knowledge acquisition, 2) causal knowledge representation, 3) causal knowledge evaluation and index, 4) causal knowledge integration, 5) and causal design knowledge management system.
This research aims to extend design, technological and computational methods in knowledge acquisition, knowledge representation, integration of knowledge, web-based knowledge management system to design problem solving processes. Results from this research are expected to advance our understanding of 1) capturing domain knowledge from experts, 2) systematic knowledge acquisition for engineering knowledge retention, 3) capturing and transforming existing procedural engineering knowledge to better knowledge representation formalism, 4) evaluating causal knowledge to make design decision, 5) comparing multiple design knowledge in heterogeneous product, 6) integrating existing design knowledge to generate refined knowledge, 7) and systematic knowledge management using information technologies and tools.
"],"dc:identifier":["https://digitalcommons.wayne.edu/oa_dissertations/135"],"dc:subject":["causal knowledge evaluation","causal knowledge evaluation index","causal knowledge integration","causal knowledge management system","causal knowledge representation","knowledge acquisition","Industrial Engineering"],"dc:title":["Causal Product Knowledge Management"],"thesis:degree_discipline":["Industrial and Manufacturing Engineering"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T05:58:42Z"}