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Semantic Relatedness Measures for Identifying Relationships in Product Center for e-Design Proceedings Center for e-Design 8-30-2009 Semantic Relatedness Measures for Identifying Relationships in Product Development Processes Paul Witherell University of Massachusetts Amherst, [email protected] Sundar Krishnamurty University of Massachusetts Amherst, [email protected] Ian R. Grosse University of Massachusetts Amherst, [email protected] Jack C. Wileden University of Massachusetts Amherst, [email protected] Follow this and additional works at: http://lib.dr.iastate.edu/edesign_conf Part of the Industrial Engineering Commons, and the Programming Languages and Compilers Commons Recommended Citation Witherell, Paul; Krishnamurty, Sundar; Grosse, Ian R.; and Wileden, Jack C., "Semantic Relatedness Measures for Identifying Relationships in Product Development Processes" (2009). Center for e-Design Proceedings. 6. http://lib.dr.iastate.edu/edesign_conf/6 This Conference Proceeding is brought to you for free and open access by the Center for e-Design at Iowa State University Digital Repository. It has been accepted for inclusion in Center for e-Design Proceedings by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Semantic Relatedness Measures for Identifying Relationships in Product Development Processes Abstract The eS mantic Web, especially in relation to ontologies, provides a structured, formal framework for knowledge interoperability. This trait has been exploited by both the biomedical community in development of the Human Gene Ontology [1] and also by geographers in development of geospatial ontologies [2]. Using semantic relatedness techniques, researchers from both communities have been able to develop and integrate comprehensive knowledge bases. Beyond knowledge integration, semantic relatedness techniques have also been able to provide each community with a unique insight into relationships between concepts in their respective domains. In the engineering community, semantic relatedness techniques promise to provide similar insight into product development processes. This paper explores the application of semantic relatedness techniques to ontologies as a means towards improved knowledge management in product development processes. Several different semantic relatedness techniques are reviewed, including a recently developed meronomic technique specific ot domain ontologies. Three of these techniques are adopted to create a semantic relatedness measure specifically designed to identify and rank underlying relationships that exist between aspects of the product development process. Four separate case studies are then presented to evaluate the relative accuracy of the developed algorithm and then determine its effectiveness in exposing underlying relationships. Keywords Product development Disciplines Industrial Engineering | Programming Languages and Compilers Comments This article is from Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (2009): Paper No. DETC2009-87624, pp. 395-408, doi:10.1115/DETC2009-87624. Posted with permission. This conference proceeding is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/edesign_conf/6 Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009 August 30 - September 2, 2009, San Diego, California, USA DETC2009-87624 SEMANTIC RELATEDNESS MEASURES FOR IDENTIFYING RELATIONSHIPS IN PRODUCT DEVELOPMENT PROCESSES Paul Witherell, Sundar Krishnamurty, Ian Grosse Jack Wileden University of Massachusetts Amherst University of Massachusetts Amherst Department of Mechanical and Industrial Engineering Department of Computer Science Amherst MA 01003 Amherst MA 01003 [email protected], [email protected], [email protected] [email protected] ABSTRACT 1. INTRODUCTION The Semantic Web, especially in relation to The successful development of a product requires the ontologies, provides a structured, formal framework for timely execution of many complex steps. At each step knowledge interoperability. This trait has been exploited by decisions are made, and their implications often affect the both the biomedical community in development of the Human many other aspects of the development process. Additionally, Gene Ontology [1] and also by geographers in development of steps of the development process are frequently revisited and geospatial ontologies [2]. Using semantic relatedness manipulated, usually resulting in further changes in techniques, researchers from both communities have been able information. While understanding the comprehensive to develop and integrate comprehensive knowledge bases. knowledge associated with individual stages of the product Beyond knowledge integration, semantic relatedness development process is important, it is equally important to techniques have also been able to provide each community understand how this knowledge interacts. with unique insights into relationships between concepts in Ideally, relationships between each stage should be their respective domains. In the engineering community, fully exposed and made computable, so that software tools can semantic relatedness techniques promise to provide similar help engineers understand these interactions and perhaps insights into product development processes. predict the impact of changes to a product. To best achieve This paper explores the application of semantic this, however, the knowledge associated with each stage must relatedness techniques to ontologies as a means towards be made explicit. This explicitness can be offered through improved knowledge management in product development formal, structured, frameworks provided by ontologies. processes. Several different semantic relatedness techniques are reviewed, including a recently developed meronomic 1.1 Relationships in Product Development technique specific to domain ontologies. Three of these Design, analysis, and manufacturing are a few of the techniques are combined to create a semantic relatedness many stages associated with the product development process. measure specifically designed to identify and rank underlying Understanding how and where these stages interact is essential relationships that exist between aspects of the product to understanding the product as whole. While some of these development process. Four separate case studies are then relationships may be considered obvious, other relationships presented to evaluate the relative accuracy of the developed may be more subtle. Two such relationship types that are algorithm and then assess its effectiveness in exposing associated with and frequently play an important role in underlying relationships. understanding the product development process include the “component of” (or “part of” relationship), and the “similar,” or “like” relationship. The ability to understand and identify similarities, or “likeness,” between product information can be extremely beneficial, as much of a product design is not original design 1 but actually redesign [3]. Similarities regularly exist between Ontologies have been adopted by both the biomedical not only new and existing products but also within a single community in development of the Human Gene Ontology [1] product at different stages of the development process. and also by geographers in development of geospatial Though recognizing similarities is important, the ability to ontologies [2] as a preferred means of knowledge recognize “part of” relationships creates an environment representation and integration. Using semantic relatedness where changes in component knowledge can be reflected in techniques, researchers in these domains have developed and assembly knowledge. Additionally, transitive associations integrated comprehensive knowledge bases for their respective made through “part of” relationships can provide insight into communities. Semantic relatedness techniques have provided downstream implications as a result of changes within an each community with unique insights into relationships which integrated knowledge framework. exist between concepts in their respective domains. In the In discussing the decision process, Mark Jennings of engineering community, relatedness techniques have been the Ford Motor Company [4] introduces a scenario that adopted as a method for improving knowledge retrieval [16], a exemplifies the importance of understanding product popular application. However, the full potential of relatedness development relationships. Jennings discusses a trade-off techniques has yet to be realized by the engineering between vehicle cabin comfort and vehicle fuel economy. community. Properly employed, these techniques can provide Jennings states one approach to improving fuel economy is new insights into the product development processes by reducing the load on the air conditioner, including: improved exposing dependencies and inter-relationships across the AC components, more intelligent control systems, and various product development disciplines. reduction of interior thermal mass (e.g. lighter seats). While Ontologies provide a framework where underlying the first two alternatives are rather intuitive, the final load relationships in the product development process can be not reduction alternative presents an interesting case. The rather only identified but also quantified. When modeled
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