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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

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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
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

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