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CO-WORD GRAPHS FOR DESIGN AND MANUFACTURE KNOWLEDGE MAPPING

Part of: Design Innovation, Information and Knowledge

Published online by Cambridge University Press:  11 June 2020

J. Gopsill ,
M. Humphrey ,
D. Thompson  and
E. Garcia
J. Gopsill*
Affiliation:
University of Bath, United Kingdom
M. Humphrey
Affiliation:
National Composites Centre, United Kingdom
D. Thompson
Affiliation:
National Composites Centre, United Kingdom
E. Garcia
Affiliation:
National Composites Centre, United Kingdom
*
*J.A.Gopsill@bath.ac.uk

Abstract

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Design & Manufacture Knowledge Mapping is a critical activity in medium-to-large organisations supporting many organisational activities. However, techniques for effective mapping of knowledge often employ interviews, consultations and appraisals. Although invaluable in providing expert insight, the application of such methods is inherently intrusive and resource intensive. This paper presents word co-occurrence graphs as a means to automatically generate knowledge maps from technical documents and validates against expert generated knowledge maps.

Keywords

knowledge managementtechnology developmentdesign informaticsgraph theory
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 1275 - 1284
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Björnfot, A. and Stehn, L. (2007), “A design structural matrix approach displaying structural and assembly requirements in construction: A timber case study”, Journal of Engineering Design, Vol. 18 No. 2, pp. 113–124.CrossRefGoogle Scholar
Blondel, V.D. et al. (2008), “Fast unfolding of communities in large networks”, Journal of Statistical Mechanics: Theory and Experiment, Vol. 2008 No. 10, p. 10008.CrossRefGoogle Scholar
Deibel, K., Anderson, R. and Anderson, R. (2005), “Using edit distance to analyze card sorts”, Expert Systems, Vol. 22 No. 3, pp. 129–138. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1468-0394.2005.00304.xCrossRefGoogle Scholar
Ding, Y., Chowdhury, G.G. and Foo, S. (2001), “Bibliometric cartography of information retrieval research by using co-word analysis”, Information processing & management, Vol. 37 No. 6, pp. 817–842.CrossRefGoogle Scholar
Dori, D. and Shpitalni, M. (2005), “Mapping knowledge about product lifecycle engineering for ontology construction via object-process methodology”, CIRP annals, Vol. 54 No. 1, pp. 117–122.CrossRefGoogle Scholar
Galil, Z. (1986), “Efficient algorithms for finding maximum matching in graphs”, ACM Computing Surveys (CSUR), Vol. 18 No. 1, pp. 23–38.CrossRefGoogle Scholar
Gopsill, J. et al. (2015), “The evolution of terminology within a large distributed engineering project”, In International conference on engineering design (iced15).Google Scholar
Gopsill, J.A. et al. (2016), “Automatic generation of design structure matrices through the evolution of product models”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 30 No. 4, pp. 424–445.CrossRefGoogle Scholar
Hagberg, A.A., Schult, D.A. and Swart, P.J. (2008), “Exploring network structure, dynamics, and function using NetworkX”, In Proceedings of the 7th python in science conference (scipy2008), pp. 11–15.Google Scholar
He, Y. et al. (2019), “Mining and Representing the Concept Space of Existing Ideas for Directed Ideation”, Journal of Mechanical Design, Vol. 141 No. 12, p. 121101.CrossRefGoogle Scholar
Jafari, M. et al. (2009), “A framework for the selection of knowledge mapping techniques”, Journal of Knowledge Management Practice, Vol. 10, p. 1.Google Scholar
Jones, D.E. et al. (2015), “Improving enterprise wide search in large engineering multinationals: A linguistic comparison of the structures of internet-search and enterprise-search queries”, In Ifip international conference on product lifecycle management, Springer, pp. 216–226.Google Scholar
Jones, S.L. et al. (2015), Subject lines as sensors: Co-word analysis of email to support the management of collaborative engineering work. In Ds 80-6 proceedings of the 20th international conference on engineering design (iced 15) vol 6: Design methods and tools-part 2, 27-30 July 2015, pp. 307–318.Google Scholar
Le, Q. and Mikolov, T. (2014), “Distributed representations of sentences and documents”, International conference on machine learning, pp. 1188–1196.Google Scholar
Liu, Y. et al. (2014), “Chi 1994-2013: Mapping two decades of intellectual progress through co-word analysis”, In Proceedings of the 32nd annual acm conference on human factors in computing systems, pp. 3553–3562. ACM.CrossRefGoogle Scholar
Newman, M.E.J. (2004), “Analysis of weighted networks”, Physical Review E, Vol. 70 No. 5, p. 056131.CrossRefGoogle ScholarPubMed
Newman, M.E.J. (2006), “Modularity and community structure in networks”, Proceedings of the National Academy of Sciences, Vol. 103 No. 23, pp. 8577–8582.CrossRefGoogle ScholarPubMed
Sarica, S., Luo, J. and Wood, K.L. (2020), “Technet: Technology semantic network based on patent data”, Expert Systems with Applications, Vol. 142, p. 112995.CrossRefGoogle Scholar
Sarica, S. et al. (2019), “Engineering knowledge graph for keyword discovery in patent search”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 2249–2258.Google Scholar
Schmidt, D.M. et al. (2015), “Identification of knowledge and processes in design projects”, In Ds 80-10 proceedings of the 20th international conference on engineering design (iced 15) vol 10: Design information and knowledge management, 27-30 July 2015, pp. 283–292.Google Scholar
Schmidt, D.M. et al. (2013a), “Interpreting knowledge maps using structural criteria”, In Ds 75-6: Proceedings of the 19th international conference on engineering design (iced13), design for harmonies, vol. 6: Design information and knowledge, 19-22 August 2013.Google Scholar
Schmidt, D.M. et al. (2013b), “Multiple-domain matrices and knowledge maps for visualizing knowledge-driven scenarios”, In Reducing risk in innovation: Proceedings of the 15th international dsm conference, 28-30 August 2013, p. 55.CrossRefGoogle Scholar
von Saucken, C. et al. (2014), “Measures and methods for systematic knowledge management”, In Ds 77: Proceedings of the design 2014 13th international design conference, pp. 1925–1936.Google Scholar
Yan, B. and Luo, J. (2019), “Multicores-periphery structure in networks”, Network Science, Vol. 7 No. 1, pp. 70–87.CrossRefGoogle Scholar