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Supervised Process of Un-structured Data Analysis for Knowledge Chaining

Abstract : Along the product life-cycle, industrial processes generate massive digital assets containing precious information. Besides structured databases, written reports hold unstructured information hardly exploitable due to the lack of vocabulary and syntax standardization. In this paper we present a methodology and natural language processing approach to exploit these documents. Our method consists in providing connections based on supervised retrieval of domain-specific expressions. No prior document analysis are required to drive the algorithm. It underlines a scale of specificity in pattern visualization. This allows relevant and specific information extraction for feedback (e.g. design stage, after-sales service).
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https://hal.archives-ouvertes.fr/hal-01347030
Contributor : Matthieu Quantin <>
Submitted on : Thursday, September 22, 2016 - 10:14:58 AM
Last modification on : Friday, April 10, 2020 - 8:20:21 AM
Long-term archiving on: : Friday, December 23, 2016 - 12:26:24 PM

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Matthieu Quantin, Benjamin Hervy, Florent Laroche, Alain Bernard. Supervised Process of Un-structured Data Analysis for Knowledge Chaining. CIRP design conference, KTH, Jun 2016, Stockholm, Sweden. pp.436-441, ⟨10.1016/j.procir.2016.04.123⟩. ⟨hal-01347030⟩

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