{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T11:12:02Z","timestamp":1756897922760},"reference-count":41,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2019,3,19]],"date-time":"2019-03-19T00:00:00Z","timestamp":1552953600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1017\/s0890060419000027","type":"journal-article","created":{"date-parts":[[2019,3,19]],"date-time":"2019-03-19T07:10:21Z","timestamp":1552979421000},"page":"289-301","source":"Crossref","is-referenced-by-count":7,"title":["Automated feedback generation for formal manufacturing rule extraction"],"prefix":"10.1017","volume":"33","author":[{"given":"SungKu","family":"Kang","sequence":"first","affiliation":[]},{"given":"Lalit","family":"Patil","sequence":"additional","affiliation":[]},{"given":"Arvind","family":"Rangarajan","sequence":"additional","affiliation":[]},{"given":"Abha","family":"Moitra","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Dean","family":"Robinson","sequence":"additional","affiliation":[]},{"given":"Debasish","family":"Dutta","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2019,3,19]]},"reference":[{"key":"S0890060419000027_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2011.03.043"},{"key":"S0890060419000027_ref3","unstructured":"Apache Jena. Retrieved July 4, 2017, Available at https:\/\/jena.apache.org\/."},{"key":"S0890060419000027_ref1","doi-asserted-by":"publisher","DOI":"10.1115\/1.2720879"},{"key":"S0890060419000027_ref18","unstructured":"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. V01BT02A033\u2013V01BT02A033). American Society of Mechanical Engineers."},{"key":"S0890060419000027_ref11","doi-asserted-by":"publisher","DOI":"10.1142\/S1793351X13500025"},{"key":"S0890060419000027_ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-51133-7_91"},{"key":"S0890060419000027_ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-03037-0_7"},{"key":"S0890060419000027_ref8","unstructured":"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. V02AT03A011\u2013V02AT03A011). American Society of Mechanical Engineers."},{"key":"S0890060419000027_ref2","doi-asserted-by":"publisher","DOI":"10.1115\/1.4027582"},{"key":"S0890060419000027_ref6","volume-title":"Feedback in Higher and Professional Education: Understanding it and Doing it Well","author":"Boud","year":"2013"},{"key":"S0890060419000027_ref7","volume-title":"Design for Manufacturability Handbook","author":"Bralla","year":"1998"},{"key":"S0890060419000027_ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.04.014"},{"key":"S0890060419000027_ref10","first-page":"83","article-title":"Student modeling and tutoring flexibility in the Lisp Intelligent Tutoring System","author":"Corbett","year":"1990","journal-title":"Intelligent tutoring systems: At the crossroads of artificial intelligence and education"},{"key":"S0890060419000027_ref32","doi-asserted-by":"crossref","unstructured":"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. V004T05A001\u2013V004T05A001). American Society of Mechanical Engineers.","DOI":"10.1115\/DETC2013-12028"},{"key":"S0890060419000027_ref4","unstructured":"Apache OpenNLP. Retrieved July 4, 2017, Available at https:\/\/opennlp.apache.org\/."},{"key":"S0890060419000027_ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-014-0017-9"},{"key":"S0890060419000027_ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2005.01.004"},{"key":"S0890060419000027_ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.11.058"},{"key":"S0890060419000027_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2016.01.002"},{"key":"S0890060419000027_ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2899415.2899422"},{"key":"S0890060419000027_ref20","doi-asserted-by":"publisher","DOI":"10.1080\/08993400500224286"},{"key":"S0890060419000027_ref21","unstructured":"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. 43\u201350."},{"key":"S0890060419000027_ref23","doi-asserted-by":"publisher","DOI":"10.1115\/DETC2005-85107"},{"key":"S0890060419000027_ref24","unstructured":"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)."},{"key":"S0890060419000027_ref25","first-page":"37","article-title":"A methodology for engineering ontology acquisition and validation","volume":"23","author":"Li","year":"2009","journal-title":"AI EDAM"},{"key":"S0890060419000027_ref26","unstructured":"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. 317\u2013321)."},{"key":"S0890060419000027_ref28","unstructured":"Nagata N (2002) Banzai: Computer assisted sentence production practice with intelligent feedback. Computer assisted system for teaching and learning Japanese, 2002."},{"key":"S0890060419000027_ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s00766-011-0119-y"},{"key":"S0890060419000027_ref30","volume-title":"OWL: Web Ontology Language","year":"2011"},{"key":"S0890060419000027_ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-33111-9_19"},{"key":"S0890060419000027_ref33","unstructured":"RDF 1.1 Concepts and Abstract Syntax (2014) RDF 1.1 Concepts and Abstract Syntax. W3C\u2014World Wide Web Consortium. Available at https:\/\/www.w3.org\/TR\/rdf11-concepts\/."},{"key":"S0890060419000027_ref34","unstructured":"RDF Schema 1.1. (2014) RDF Schema 1.1. W3C\u2014World Wide Web Consortium. Available at https:\/\/www.w3.org\/TR\/rdf-schema\/."},{"key":"S0890060419000027_ref37","unstructured":"The Natural Language Processing for JVM languages (NLP4J). Retrieved July 4, 2017, Available at https:\/\/emorynlp.github.io\/nlp4j\/."},{"key":"S0890060419000027_ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.09.124"},{"key":"S0890060419000027_ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2016.10.053"},{"key":"S0890060419000027_ref41","first-page":"33","article-title":"A rough-set-refined text mining approach for crude oil market tendency forecasting","volume":"2","author":"Yu","year":"2005","journal-title":"International Journal of Knowledge and Systems Sciences"},{"key":"S0890060419000027_ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-005-0003-9"},{"key":"S0890060419000027_ref22","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060407070199"},{"key":"S0890060419000027_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2008.10.012"},{"key":"S0890060419000027_ref35","first-page":"1","article-title":"A knowledge mining approach to document classification","volume":"2","author":"Riel","year":"2009","journal-title":"The Asian International Journal of Science and Technology in Production and Manufacturing"},{"key":"S0890060419000027_ref27","first-page":"409","article-title":"DB-suite: experiences with three intelligent, web-based database tutors","volume":"15","author":"Mitrovic","year":"2004","journal-title":"Journal of Interactive Learning Research"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060419000027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T23:47:36Z","timestamp":1663112856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060419000027\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,19]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["S0890060419000027"],"URL":"https:\/\/doi.org\/10.1017\/s0890060419000027","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,19]]}}}