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Automated feedback generation for formal manufacturing rule extraction

Published online by Cambridge University Press:  19 March 2019

SungKu Kang ,
Lalit Patil ,
Arvind Rangarajan ,
Abha Moitra ,
Tao Jia ,
Dean Robinson  and
Debasish Dutta
SungKu Kang*
Affiliation:
University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
Lalit Patil
Affiliation:
University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
Arvind Rangarajan
Affiliation:
General Electric Global Research, Niskayuna, New York 12309, USA
Abha Moitra
Affiliation:
General Electric Global Research, Niskayuna, New York 12309, USA
Tao Jia
Affiliation:
General Electric Healthcare, Waukesha, Wisconsin 53188, USA
Dean Robinson
Affiliation:
General Electric Global Research, Niskayuna, New York 12309, USA
Debasish Dutta
Affiliation:
Rutgers University, New Brunswick, New Jersey 08901, USA
*
Author for correspondence: SungKu Kang, E-mail: skang47@illinois.edu
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Abstract

Manufacturing knowledge is maintained primarily in the unstructured text in industry. To facilitate the reuse of the knowledge, previous efforts have utilized Natural Language Processing (NLP) to classify manufacturing documents or to extract structured knowledge (e.g. ontology) from manufacturing text. On the other hand, extracting more complex knowledge, such as manufacturing rule, has not been feasible in a practical scenario, as standard NLP techniques cannot address the input text that needs validation. Specifically, if the input text contains the information irrelevant to the rule-definition or semantically invalid expression, standard NLP techniques cannot selectively derive precise information for the extraction of the desired formal manufacturing rule. To address the gap, we developed the feedback generation method based on Constraint-based Modeling (CBM) coupled with NLP and domain ontology, designed to support formal manufacturing rule extraction. Specifically, the developed method identifies the necessity of input text validation based on the predefined constraints and provides the relevant feedback to help the user modify the input text, so that the desired rule can be extracted. We proved the feasibility of the method by extending the previously implemented formal rule extraction framework. The effectiveness of the method is demonstrated by enabling the extraction of correct manufacturing rules from all the cases that need input text validation, about 30% of the dataset, after modifying the input text based on the feedback. We expect the feedback generation method will contribute to the adoption of semantics-based technology in the manufacturing field, by facilitating precise knowledge acquisition from manufacturing-related documents in a practical scenario.

Keywords

Automated feedback generationNatural Language Processingsemantic technologyontology

Information

Type
Research Article
Information
AI EDAM , Volume 33 , Issue 3 , August 2019 , pp. 289 - 301
Copyright
Copyright © Cambridge University Press 2019 

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