journal article·2023-01-25Open access

Building a Design-Rationale-Centric Knowledge Network to Realize the Internalization of Explicit Knowledge

Gaofeng Yue, Jihong Liu, Qiang Zhang, Yongzhu Hou

Applied Sciences· Volume 13· Issue 3

Abstract

A large number of publicly available documents, including patent documents and journal articles, can provide designers with creative stimuli, which could facilitate product innovation and collaborative design. As an important tacit knowledge, the acquisition, sharing, and reuse of design rationale (DR) is of great value to designers, which could help designers to better understand design intentions and ideas, support design automation, and promote better collaborative design. However, due to the fragmentation of DR in documentation, this hinders designer acquisition and reuse. If the DR fragments could be automatically extracted from the technical documents to build an interconnected knowledge network system, the problem would be effectively solved, which would further promote the development and utilization of digital archives. To address this issue, this study proposes a three-dimensional design knowledge network metamodel, Design Knowledge Semantic Network (DKSN), and a DKSN-based knowledge fusion method for the construction of a Design Knowledge Network (DKN). We set up an empirical experiment to verify the feasibility and performance of the method. Patent documents and open access research articles are used as sample documents, and a product data dictionary imported from ISO/TS 23768-1 is used as the predefined artifact dictionary. The results further confirm the feasibility and good application prospects of the proposed method.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Manufacturing Process and Optimization · Design Education and Practice · Product Development and Customization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China

China National Institute of Standardization, Beijing 100191, China

Beijing Institute of Mechanical and Electrical Engineering, Beijing 100072, China

Funding

Ministry of Science and Technology of the Republic of China

References (72)

Show source-provided references
1. (2008). Standard No. BS 7000-1:2008.
2. Boundaries Knowledge (Knowing)—A Source of Business Innovation10.1002/kpm.1603 ↗
3. Capturing Design Rationale10.1016/j.cad.2008.10.005 ↗
4. Setchi, R., and Bouchard, C. (2010). In Search of Design Inspiration: A Semantic-Based Approach. J. Comput. Inf. Sci. Eng., 10.10.1115/1.3482061 ↗
5. A New Function-Based Patent Knowledge Retrieval Tool for Conceptual Design of Innovative Products10.1016/j.compind.2019.103154 ↗
6. A New Method for Extracting Knowledge from Patents to Inspire Designers during the Problem-Solving Phase10.1080/09544828.2017.1316361 ↗
7. Goucher-Lambert, K., Gyory, J.T., Kotovsky, K., and Cagan, J. (2020). Adaptive Inspirational Design Stimuli: Using Design Output to Computationally Search for Stimuli That Impact Concept Generation. J. Mech. Des., 142.10.1115/1.4046077 ↗
8. Patent Stimuli Search and Its Influence on Ideation Outcomes10.1017/dsj.2017.27 ↗
9. Sources of Inspiration: A Language of Design10.1016/S0142-694X(00)00022-3 ↗
10. Inspirational Stimuli Improve Idea Fluency during Ideation: A Replication and Extension Study with Eye-Tracking10.1017/pds.2022.88 ↗
11. Semantic Relation Based Personalized Ranking Approach for Engineering Document Retrieval10.1016/j.aei.2015.01.003 ↗
12. A Hypernetwork-Based Approach to Collaborative Retrieval and Reasoning of Engineering Design Knowledge10.1016/j.aei.2019.100956 ↗
13. Thomas, R.G., and Daniel, R. (1991). Design Knowledge and Design Rationale: A Framework for Representation, Capture, and Use, Knowledge Systems Laboratory, Computer Science Department, Stanford University.
14. Dworschak, F., Kügler, P., Schleich, B., and Wartzack, S. (2021). Model and Knowledge Representation for the Reuse of Design Process Knowledge Supporting Design Automation in Mass Customization. Appl. Sci., 11.10.3390/app11219825 ↗
15. Luo, Y. (2018). Design Rationale Knowledge Management: A Survey, Springer International Publishing.
16. Carroll, J.M. (2013). Creativity and Rationale: Enhancing Human Experience by Design, Springer.10.1007/978-1-4471-4111-2 ↗
17. Capturing, Structuring and Accessing Design Rationale in Integrated Product Design and Manufacturing Processes10.1016/j.aei.2016.06.004 ↗
18. From Form Features to Semantic Features in Existing MCAD: An Ontological Approach10.1016/j.aei.2020.101088 ↗
19. A Social Media Framework to Support Engineering Design Communication10.1016/j.aei.2013.07.002 ↗
20. An Approach to Capturing and Reusing Tacit Design Knowledge Using Relational Learning for Knowledge Graphs10.1016/j.aei.2021.101505 ↗
21. Automated Generation of Engineering Rationale, Knowledge and Intent Representations during the Product Life Cycle10.1007/s10055-011-0196-8 ↗
22. Systematization of Design Knowledge10.1016/S0007-8506(07)62409-3 ↗
23. Systemic Design Principles in Social Innovation: A Study of Expert Practices and Design Rationales10.1016/j.sheji.2020.06.001 ↗
24. A Process-Oriented Approach to Design Rationale10.1207/s15327051hci0603&4_6 ↗
25. Formality Considered Harmful: Experiences, Emerging Themes, and Directions on the Use of Formal Representations in Interactive Systems10.1023/A:1008716330212 ↗
26. Lee, J. (1989). Decision Representation Language (DRL) and Its Support Environment, MIT Artificial Intelligence Laboratory. MIT Artificial Intelligence Laboratory Working Papers, WP-325.
27. GIBIS: A Hypertext Tool for Exploratory Policy Discussion10.1145/58566.59297 ↗
28. The Retrieval of Structured Design Rationale for the Re-Use of Design Knowledge with an Integrated Representation
29. Integrating and Navigating Engineering Design Decision-Related Knowledge Using Decision Knowledge Graph10.1016/j.aei.2021.101366 ↗
30. Towards the Derivation of Verbal Content Relations from Patent Claims Using Deep Syntactic Structures10.1016/j.knosys.2011.05.014 ↗
31. Gero, J.S., and Hanna, S. (2015). Using Text Mining Techniques to Extract Rationale from Existing Documentation, Springer International Publishing.
32. Using Evolutionary Algorithms to Select Text Features for Mining Design Rationale10.1017/S0890060420000037 ↗
33. On User Rationale in Software Engineering10.1007/s00766-018-0293-2 ↗
34. Liu, Y., Liang, Y., Kwong, C.K., and Lee, W.B. (2010). A New Design Rationale Representation Model for Rationale Mining. J. Comput. Inf. Sci. Eng., 10.10.1115/1.3470018 ↗
35. A Semantic Representation Model for Design Rationale of Products10.1016/j.aei.2012.10.005 ↗
36. Dilemmas in a General Theory of Planning10.1007/BF01405730 ↗
37. Kunz, W., and Rittel, H.W.J. (1970). Issues as Elements of Information Systems, Institute of Urban and Regional Development, University of California.
38. Questions, Options, and Criteria: Elements of Design Space Analysis10.1207/s15327051hci0603&4_2 ↗
39. Kuaba Approach: Integrating Formal Semantics and Design Rationale Representation to Support Design Reuse10.1017/S0890060408000279 ↗
40. From General Design Theory to Knowledge-Intensive Engineering10.1017/S0890060400000998 ↗
41. A Reuse Oriented Representation Model for Capturing and Formalizing the Evolving Design Rationale10.1017/S0890060413000395 ↗
42. A Functional Feature Modeling Method10.1016/j.aei.2017.04.003 ↗
43. On the Role of Generating Textual Description for Design Intent Communication in Feature-Based 3D Collaborative Design10.1016/j.aei.2019.02.003 ↗
44. What’s in Design Rationale?10.1207/s15327051hci0603&4_3 ↗
45. A Semantic Network Cartography of the Creative Mind10.1016/j.tics.2019.01.007 ↗
46. Investigating the Structure of Semantic Networks in Low and High Creative Persons10.3389/fnhum.2014.00407 ↗
47. A Novel Function-Structure Concept Network Construction and Analysis Method for a Smart Product Design System10.1016/j.aei.2021.101502 ↗
48. Siemens, G. (2023, January 12). Knowing Knowledge; Lulu.com. Available online: https://www.knowingknowledge.com/.
49. Supporting Connectivism in Knowledge Based Engineering with Graph Theory, Filtering Techniques and Model Quality Assurance10.1016/j.aei.2018.07.005 ↗
50. Design knowledge representation with technology semantic network10.1017/pds.2021.104 ↗
51. Design Structure Network (DSN): A Method to Make Explicit the Product Design Specification Process for Mass Customization10.1007/s00163-020-00331-y ↗
52. An Automatic Literature Knowledge Graph and Reasoning Network Modeling Framework Based on Ontology and Natural Language Processing10.1016/j.aei.2019.100959 ↗
53. Zhao, D., and Andreas, S. (2015). Analysis and Visualization of Citation Networks, Springer. [1st ed.]. Synthesis Lectures on Information Concepts, Retrieval, and Services.10.1007/978-3-031-02291-3 ↗
54. Long-Term Knowledge Evolution Modeling for Empirical Engineering Knowledge10.1016/j.aei.2017.08.001 ↗
55. Multilevel Graph-Based Decision Making in Big Scholarly Data: An Approach to Identify Expert Reviewer, Finding Quality Impact Factor, Ranking Journals and Researchers10.1109/TETC.2018.2869458 ↗
56. Singhal, A. (2021, November 04). 2012 Introducing the Knowledge Graph: Things, Not Strings. Available online: https://www.blog.google/products/search/introducing-knowledge-graph-things-not/.
57. Knowledge Graphs10.1145/3447772 ↗
58. Knowledge Graph Fusion for Smart Systems: A Survey10.1016/j.inffus.2020.03.014 ↗
59. A Knowledge Graph-Based Data Representation Approach for IIoT-Enabled Cognitive Manufacturing10.1016/j.aei.2021.101515 ↗
60. An End-to-End Tabular Information-Oriented Causality Event Evolutionary Knowledge Graph for Manufacturing Documents10.1016/j.aei.2021.101441 ↗
61. Lödding, H., Riedel, R., Thoben, K.-D., von Cieminski, G., and Kiritsis, D. (2017). Knowledge Fusion of Manufacturing Operations Data Using Representation Learning, Springer International Publishing.
62. Qiu, M. (2020). Proceedings of the Algorithms and Architectures for Parallel Processing, Springer International Publishing.
63. Nonaka, I., and Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press. [1st ed.].10.1016/0024-6301(96)81509-3 ↗
64. Nonaka, I., Toyama, R., Hirata, T., Bigelow, S.J., Hirose, A., and Kohlbacher, F. (2008). Managing Flow: A Process Theory of the Knowledge-Based Firm, Palgrave Macmillan UK.10.1057/9780230583702 ↗
65. Design as Learning—Or “Knowledge Creation”—The SECI Model10.1145/1897239.1897256 ↗
66. Vătămănescu, E.-M., Bratianu, C., Dabija, D.-C., and Popa, S. (J. Knowl. Manag., 2022). Capitalizing Online Knowledge Networks: From Individual Knowledge Acquisition towards Organizational Achievements, J. Knowl. Manag., ahead-of-print.10.1108/JKM-04-2022-0273 ↗
67. Simon, H.A. (1996). The Sciences of the Artificial, MIT Press.
68. Design Rationale Systems: Understanding the Issues10.1109/64.592267 ↗
69. Reimers, N., and Gurevych, I. (2019). Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics.
70. You, Y., Li, J., Reddi, S.J., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., and Hsieh, C.-J. (2020). Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes. arXiv.
71. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics.
72. Learning the “Whys”: Discovering Design Rationale Using Text Mining—An Algorithm Perspective10.1016/j.cad.2011.08.002 ↗