Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-15T06:47:52.659Z Has data issue: false hasContentIssue false
  • English
  • Français

A KNOWLEDGE GRAPH AND RULE BASED REASONING METHOD FOR EXTRACTING SAPPHIRE INFORMATION FROM TEXT

Published online by Cambridge University Press:  19 June 2023

Kausik Bhattacharya and
Amaresh Chakrabarti
Kausik Bhattacharya*
Affiliation:
Indian Institute of Science Bangalore
Amaresh Chakrabarti
Affiliation:
Indian Institute of Science Bangalore
*
Bhattacharya, Kausik Indian Institute of Science Bangalore, India, kausikb@iisc.ac.in

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Representation of design information using causal ontologies is very effective for creative ideation in product design. Hence researchers created databases with models of engineering and biological systems using causal ontologies. Manually building many models using technical documents requires significant effort by specialists. Researchers worked on the automatic extraction of design information leveraging the computational techniques of Machine Learning. But these methods are data intensive, have manual touch points and have not yet reported the end-to-end performance of the process. In this paper, we present the results of a new method inspired by the cognitive process followed by specialists. This method uses the Knowledge Graph with Rule based reasoning for information extraction for the SAPPhIRE causality model from natural language texts. Unlike the supervised learning methods, this new method does not require data intensive modelling. We report the performance of the end-to-end information extraction process, which is found to be a promising alternative.

Keywords

Computational design methodsCreativityOntologiesInformation ExtractionMachine learning

Information

Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023, pp. 221 - 230
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), 2023. Published by Cambridge University Press

References

Baldussu, A., Cascini, G., Rosa, F., & Rovida, E., (2012), “Causal models for bio-inspired design: a comparison”, DS 70: Proceedings of DESIGN 2012, the 12th International Design Conference, Dubrovnik, Croatia (pp. 717–726).Google Scholar
Bhattacharya, K., Bhatt, A.N., Ranjan, B.S.C., Keshwani, S., Srinivasan, V. and Chakrabarti, A., (2023), “Extracting Information for Creating SAPPhIRE Model of Causality from Natural Language Descriptions”, Design Computing and Cognition'22 (pp. 3–20). https://doi.org/10.1007/978-3-031-20418-0_1CrossRefGoogle Scholar
Cascini, G., & Zini, M., (2011), “Computer-aided comparison of thesauri extracted from complementary patent classes as a means to identify relevant field parameters”, Global Product Development (pp. 555–566). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15973-2_56CrossRefGoogle Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B. S., (2005), “A functional representation for aiding biomimetic and artificial inspiration of new ideas”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 19(2), 113–132. https://doi.org/10.1017/s0890060405050109CrossRefGoogle Scholar
Cheong, H. and Shu, L.H., (2012), “Automatic extraction of causally related functions from natural-language text for biomimetic design”, In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 45066, pp. 373–382). American Society of Mechanical Engineers. https://doi.org/10.1115/detc2012-70732CrossRefGoogle Scholar
Cowie, J. and Lehnert, W., (1996), “Information extraction”, Communications of the ACM, 39(1), pp.80–91. https://doi.org/10.1145/234173.234209CrossRefGoogle Scholar
Fu, K., Moreno, D., Yang, M. and Wood, K.L., (2014), “Bio-inspired design: an overview investigating open questions from the broader field of design-by-analogy”, Journal of Mechanical Design, 136(11), p.111102. https://doi.org/10.1115/1.4028289CrossRefGoogle Scholar
Gangemi, A., (2013), “A comparison of knowledge extraction tools for the semantic web”, Extended Semantic Web Conference (pp. 351–366). Springer, Berlin. https://doi.org/10.1007/978-3-642-38288-8_24CrossRefGoogle Scholar
Goel, A., Hagopian, K., Zhang, S., & Rugaber, S., (2022), “Towards a virtual librarian for biologically inspired design”, Design Computing and Cognition'20 (pp. 369–386). Springer, Cham. https://doi.org/10.1007/978-3-030-90625-2_21CrossRefGoogle Scholar
Goel, A., Rugaber, S., & Vattam, S., (2009), “Structure, behavior & function of complex systems: The SBF modeling language”, Artificial Intelligence in Engineering Design, Analysis and Manufacturing, 23(1), 23–35. https://doi.org/10.1017/s0890060409000080CrossRefGoogle Scholar
Goel, A.K. and Shu, L.H., (2015), “Analogical thinking: An introduction in the context of design”, Artificial Intelligence in Engineering Design, Analysis and Manufacturing, 29(2), pp.133–134. https://doi.org/10.1017/s0890060415000013CrossRefGoogle Scholar
Gonçalves, M., Cardoso, C. and Badke-Schaub, P., (2016), “Inspiration choices that matter: the selection of external stimuli during ideation”, Design Science, 2. https://doi.org/10.1017/dsj.2016.10CrossRefGoogle Scholar
Han, J., Sarica, S., Shi, F. and Luo, J., (2022), “Semantic Networks for Engineering Design: State of the Art and Future Directions”, Journal of Mechanical Design, 144(2). https://doi.org/10.1115/1.4052148Google Scholar
Halevy, A., Norvig, P., & Pereira, F., (2009), “The Unreasonable Effectiveness of Data”, IEEE Intelligent Systems, 24(2), 8–12. https://doi.org/10.1109/mis.2009.36CrossRefGoogle Scholar
Howard, T.J., Dekoninck, E.A. and Culley, S.J., (2010), “The use of creative stimuli at early stages of industrial product innovation.”, Research in Engineering design, 21(4), pp.263–274. https://doi.org/10.1007/s00163-010-0091-4CrossRefGoogle Scholar
Hsieh, S.H., Lin, H.T., Chi, N.W., Chou, K.W. and Lin, K.Y., (2011), “Enabling the development of base domain ontology through extraction of knowledge from engineering domain handbooks”, Advanced Engineering Informatics, 25(2), pp.288–296. https://doi.org/10.1016/j.aei.2010.08.004CrossRefGoogle Scholar
Hubka, V. and Eder, W.E., (2002), “Theory of technical systems and engineering design synthesis”, Engineering design synthesis (pp. 49–66). Springer, London. https://doi.org/10.1007/978-1-4471-3717-7_4CrossRefGoogle Scholar
Keshwani, Sonal., (2018), Supporting Designers in Generating Novel Ideas at the Conceptual Stage Using Analogies from the Biological Domain., IISc Bengaluru PhD Thesis (https://etd.iisc.ac.in/handle/2005/4205)Google Scholar
Li, Z. and Ramani, K., (2007), “Ontology-based design information extraction and retrieval”, Artificial Intelligence Engineering Design Analysis and Manufacturing, 21(2), pp. 137–154. https://doi.org/10.1017/s0890060407070199CrossRefGoogle Scholar
Moens, M.F., (2006), Information extraction: algorithms and prospects in a retrieval context, Heidelberg: Springer., 1–22. https://doi.org/10.1007/978-1-4020-4993-4_1Google Scholar
Nédellec, C., Nazarenko, A. and Bossy, R., (2009), “Information extraction”, In Handbook on ontologies (pp. 663–685). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92673-3_30CrossRefGoogle Scholar
Russell, J, and Norvig, S., P., (2010), Artificial Intelligence A Modern Approach, Third Edition, Prentice HallGoogle Scholar
Sarica, S. and Luo, J., (2021), “Design knowledge representation with technology semantic network”, Proceedings of the Design Society, 1, pp.1043-1052. https://doi.org/10.1017/pds.2021.104CrossRefGoogle Scholar
Siddharth, L., Blessing, L., Wood, K.L. and Luo, J., (2022), “Engineering knowledge graph from patent database”, Journal of Computing and Information Science in Engineering, 22(2). https://doi.org/10.1115/1.4052293CrossRefGoogle Scholar
Tang, J., Hong, M., Zhang, D.L. and Li, J., (2008), “Information extraction: Methodologies and applications”, Emerging Technologies of Text Mining: Techniques and Applications (pp. 1–33). IGI Global. https://doi.org/10.4018/978-1-59904-373-9.ch001CrossRefGoogle Scholar
Vargas-Vera, M., Motta, E., Domingue, J., Shum, S.B. and Lanzoni, M., (2001), “Knowledge Extraction by Using an Ontology Based Annotation Tool”, In Semannot@ K-CAP 2001.Google Scholar
Vattam, S. and Goel, A. (2013), “Seeking bioinspiration online: a descriptive account”, DS 75-7: Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol. 7: Human Behaviour in Design, Seoul, Korea, 19-22.08. 2013.Google Scholar
Venkataraman, S. and Chakrabarti, A., (2009), “Sapphire–an approach to analysis and synthesis”, In DS 58-2: Proceedings of ICED 09, the 17th International Conference on Engineering Design, Vol. 2, Design Theory and Research Methodology, Palo Alto, CA, USA, 24.-27.08. 2009 (pp. 417–428).Google Scholar
Wimalasuriya, D.C. and Dou, D., (2010), “Ontology-based information extraction: An introduction and a survey of current approaches”, Journal of Information Science, 36(3), pp.306–323. https://doi.org/10.1177/0165551509360123CrossRefGoogle Scholar
Yang, J., Kim, E., Hur, M., Cho, S., Han, M. and Seo, I., (2018), “Knowledge extraction and visualization of digital design process”, Expert Systems with Applications, 92, pp. 206–215. https://doi.org/10.1016/j.eswa.2017.09.002CrossRefGoogle Scholar
Zhang, C., Zhou, G., Chang, F. and Yang, X., (2020), “Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach”, The International Journal of Advanced Manufacturing Technology, 106(5), pp.2535–2551. https://doi.org/10.1007/s00170-019-04772-1CrossRefGoogle Scholar