Skip to main content Accessibility help
×
Home

Automated feedback generation for formal manufacturing rule extraction

  • SungKu Kang (a1), Lalit Patil (a1), Arvind Rangarajan (a2), Abha Moitra (a2), Tao Jia (a3), Dean Robinson (a2) and Debasish Dutta (a4)...

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.

Copyright

Corresponding author

Author for correspondence: SungKu Kang, E-mail: skang47@illinois.edu

References

Hide All
Ahmed, S, Kim, S and Wallace, KM (2007) A methodology for creating ontologies for engineering design. Journal of Computing and Information Science in Engineering 7, 132.
Ameri, F, Kulvatunyou, B, Ivezic, N and Kaikhah, K (2014) Ontological conceptualization based on the SKOS. Journal of Computing and Information Science in Engineering 14, 031006.
Apache Jena. Retrieved July 4, 2017, Available at https://jena.apache.org/.
Apache OpenNLP. Retrieved July 4, 2017, Available at https://opennlp.apache.org/.
Boonyasopon, P, Riel, A, Uys, W, Louw, L, Tichkiewitch, S and Preez, du, N (2011) Automatic knowledge extraction from manufacturing research publications. Cirp Annals-Manufacturing Technology 60, 477480.
Boud, D and Molloy, E (eds) (2013). Feedback in Higher and Professional Education: Understanding it and Doing it Well. Abingdon-on-Thames, UK: Routledge.
Bralla, J (1998) Design for Manufacturability Handbook. New York City, USA:McGraw Hill Professional.
Cheong, H, Li, W and Iorio, F (2016) Automated extraction of system structure knowledge from text. In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. V02AT03A011V02AT03A011). American Society of Mechanical Engineers.
Choi, S, Park, H, Kang, D, Lee, JY and Kim, K (2012) An SAO-based text mining approach to building a technology tree for technology planning. Expert Systems with Applications 39, 1144311455.
Corbett, AT, Anderson, JR and Patterson, EG (1990) Student modeling and tutoring flexibility in the Lisp Intelligent Tutoring System. Intelligent tutoring systems: At the crossroads of artificial intelligence and education, 83106.
Crapo, A and Moitra, A (2013) Toward a unified English-like representation of semantic models, data, and graph patterns for subject matter experts. International Journal of Semantic Computing 07, 215236.
Dzikovska, M, Steinhauser, N, Farrow, E, Moore, J and Campbell, G (2014) BEETLE II: deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. International Journal of Artificial Intelligence in Education 24, 284332.
Ferreira, A and Atkinson, J (2009) Designing a feedback component of an intelligent tutoring system for foreign language. Knowledge-Based Systems 22, 496501.
Gutierrez, F and Atkinson, J (2011) Adaptive feedback selection for intelligent tutoring systems. Expert Systems with Applications 38, 61466152.
Jeon, SM, Lee, JH, Hahm, GJ and Suh, HW (2016) Automatic CAD model retrieval based on design documents using semantic processing and rule processing. Computers in Industry 77, 2947.
Kang, J and Lee, JK (2005) Rule identification from Web pages by the XRML approach. Decision Support Systems 41, 205227.
Kang, S, Patil, L, Rangarajan, A, Moitra, A, Jia, T, Robinson, D and Dutta, D (2015) Extraction of manufacturing rules from unstructured text using a semantic framework. In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. V01BT02A033V01BT02A033). American Society of Mechanical Engineers.
Keuning, H, Jeuring, J and Heeren, B (2016) Towards a systematic review of automated feedback generation for programming exercises – extended version. Technical Report Series, (UU-CS-2016-001).
Lane, HC and VanLehn, K (2005) Teaching the tacit knowledge of programming to noviceswith natural language tutoring. Computer Science Education 15, 183201.
Le, NT and Pinkwart, N (2011) INCOM: A web-based homework coaching system for logic programming. In Conference on Cognition and Exploratory Learning in Digital Age. pp. 4350.
Li, Z and Ramani, K (2007) Ontology-based design information extraction and retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, 137154.
Li, Z, Liu, M, Anderson, DC and Ramani, K (2005) Semantics-based design knowledge annotation and retrieval. In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 799808). American Society of Mechanical Engineers.
Li, Z, Raskin, V and Ramani, K (2007) A methodology of engineering ontology development for information retrieval. In Proceedings of the 16th International Conference on Engineering Design (ICED'07).
Li, Z, Yang, MC and Ramani, K (2009) A methodology for engineering ontology acquisition and validation. AI EDAM 23, 3751.
MacNish, C (2002) Machine learning and visualisation techniques for inferring logical errors in student code submissions. In ICALT-2002: Proc. 2nd IEEE Int. Conf. on Advanced Learning Technologies (pp. 317321).
Mitrovic, A, Suraweera, P, Martin, B and Weerasinghe, A (2004) DB-suite: experiences with three intelligent, web-based database tutors. Journal of Interactive Learning Research 15, 409.
Nagata, N (2002) Banzai: Computer assisted sentence production practice with intelligent feedback. Computer assisted system for teaching and learning Japanese, 2002.
Ohlsson, S (1994) Constraint-based student modeling. In Greer, JE and McCalla, GI (eds), Student Modelling: The Key to Individualized Knowledge-Based Instruction. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 167189.
OWL: Web Ontology Language (2011) OWL: Web Ontology Language. Berlin/Heidelberg: Springer-Verlag.
Pinquie, R, Veron, P, Segonds, F and Croue, N (2015) Natural Language Processing of Requirements for Model-Based Product Design with ENOVIA/CATIA V6. In IFIP International Conference on Product Lifecycle Management (pp. 205215). Springer, Cham.
Rangarajan, A, Radhakrishnan, P, Moitra, A, Crapo, A and Robinson, D (2013) Manufacturability analysis and design feedback system developed using semantic framework. In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. V004T05A001V004T05A001). American Society of Mechanical Engineers.
RDF 1.1 Concepts and Abstract Syntax (2014) RDF 1.1 Concepts and Abstract Syntax. W3C—World Wide Web Consortium. Available at https://www.w3.org/TR/rdf11-concepts/.
RDF Schema 1.1. (2014) RDF Schema 1.1. W3C—World Wide Web Consortium. Available at https://www.w3.org/TR/rdf-schema/.
Riel, A and Boonyasopon, P (2009) A knowledge mining approach to document classification. The Asian International Journal of Science and Technology in Production and Manufacturing 2, 110.
Shotorbani, PY, Ameri, F, Kulvatunyou, B and Ivezic, N (2016) A hybrid method for manufacturing text mining based on document clustering and topic modeling techniques. In IFIP International Conference on Advances in Production Management Systems (pp. 777786). Springer, Cham.
The Natural Language Processing for JVM languages (NLP4J). Retrieved July 4, 2017, Available at https://emorynlp.github.io/nlp4j/.
Ur-Rahman, N and Harding, JA (2012) Textual data mining for industrial knowledge management and text classification: a business oriented approach. Expert Systems with Applications 39, 47294739.
Wang, G, Tian, X, Geng, J, Evans, R and Che, S (2016) Extraction of principle knowledge from process patents for manufacturing process innovation. Procedia CIRP 56, 193198.
Yang, H, De Roeck, A, Gervasi, V, Willis, A and Nuseibeh, B (2011) Analysing anaphoric ambiguity in natural language requirements. Requirements engineering 16(3), 163189.
Yang, MC, Wood, WH III and Cutkosky, MR (2005) Design information retrieval: a thesauri-based approach for reuse of informal design information. Engineering with Computers 21, 177192.
Yu, L, Wang, S and Lai, KK (2005) A rough-set-refined text mining approach for crude oil market tendency forecasting. International Journal of Knowledge and Systems Sciences 2, 3346.

Keywords

Automated feedback generation for formal manufacturing rule extraction

  • SungKu Kang (a1), Lalit Patil (a1), Arvind Rangarajan (a2), Abha Moitra (a2), Tao Jia (a3), Dean Robinson (a2) and Debasish Dutta (a4)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed