Extracting Information for Creating SAPPhIRE Model of Causality from Natural Language Descriptions

Bhattacharya, Kausik; Bhatt, Apoorv Naresh; Ranjan, B. S. C.; Keshwani, Sonal; Chakrabarti, Amaresh

doi:10.1007/978-3-031-20418-0_1

Cited by 1 publication

(2 citation statements)

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“…We adopted an approach that is close to the cognitive behavior of the specialists when they create the SAPPhIRE model from information given in a technical document (Bhattacharya et al, 2023). Specialists first read the document to understand the information entities given in the document and identify the causal relationship between those entities.…”

Section: Approachmentioning

confidence: 99%

“…A new process using a Knowledge Graph and Rule based reasoning for extracting words from a natural language description, representing the constructs of the SAPPhIRE model of causality, was reported earlier (Bhattacharya et al, 2023). However, the previous work studied the process performance in reducing variability in the extracted information across multiple designers.…”

Section: Ontology Basedmentioning

confidence: 99%

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A Knowledge Graph and Rule Based Reasoning Method for Extracting Sapphire Information From Text

Bhattacharya

Chakrabarti

2023

Proc. Des. Soc.

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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.

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Section: Approachmentioning

confidence: 99%

Section: Ontology Basedmentioning

confidence: 99%