{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99242"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99242","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Extraction of formal manufacturing rules from unstructured English text","abstract":"Semantics-based approaches—founded on the idea of explicitly encoding meaning separately from the data or the application code—are being applied to manufacturing, for example, to enable early manufacturability feedback. These approaches rely on formal, i.e., computer-interpretable, knowledge and rules along with the context or semantics. On the other hand, manufacturing knowledge has been maintained primarily in the form of unstructured English text. It is considered impractical for engineers to author accurate, formal, and structured manufacturing rules. Previous efforts on extracting semantics from unstructured text in manufacturing have focused exclusively on basic concept names and hierarchies. In this context, this dissertation focuses on the development of a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a formal form, from unstructured English text such as those written in manufacturing handbooks. This dissertation includes the following specific research tasks. First, it studies the problem in manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. Second, it extends the framework to complement standard Natural Language Processing (NLP) techniques with manufacturing domain knowledge to resolve ambiguities, called as domain-specific ambiguities, that are due to manufacturing-specific meanings implicit in the English text. Finally, this dissertation extends the framework to identify the cases that need input text validation, and provide the relevant feedback to the user to modify the input text for the extraction of correct rules. This research also demonstrates the extensibility of the framework. Specifically, the framework was initially developed using the subset of a manufacturing handbook only including milling, metal stamping, and die-casting sections, and then applied to the rest of the manufacturing processes including 30 sections in forming, machining, casting, molding, assembling, and finishing chapters in the book. Case studies are performed to demonstrate the feasibility of the framework on the dataset of 133 sentences. First, the feasibility of the rule extraction framework is shown by extracting correct rules from approx. 57% of the sentences. Second, the effectiveness of ambiguity resolution by complementing standard NLP techniques with manufacturing domain knowledge is demonstrated by an increasing the correct rules to 70%. Lastly, for the remaining 30% of the cases that need input text validation, relevant feedback is provided to the user to modify the input text for the extraction of the correct rules. It is expected that this research will facilitate the development of formal manufacturing knowledge including complex manufacturing rules. It will thus address an important barrier that has prevented a larger scale application and the adoption of semantic technologies in the field of manufacturing, especially for semantics-based manufacturability analysis.","abstract_html":"Semantics-based approaches—founded on the idea of explicitly encoding meaning separately from the data or the application code—are being applied to manufacturing, for example, to enable early manufacturability feedback. These approaches rely on formal, i.e., computer-interpretable, knowledge and rules along with the context or semantics. On the other hand, manufacturing knowledge has been maintained primarily in the form of unstructured English text. It is considered impractical for engineers to author accurate, formal, and structured manufacturing rules. Previous efforts on extracting semantics from unstructured text in manufacturing have focused exclusively on basic concept names and hierarchies. In this context, this dissertation focuses on the development of a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a formal form, from unstructured English text such as those written in manufacturing handbooks. This dissertation includes the following specific research tasks. First, it studies the problem in manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. Second, it extends the framework to complement standard Natural Language Processing (NLP) techniques with manufacturing domain knowledge to resolve ambiguities, called as domain-specific ambiguities, that are due to manufacturing-specific meanings implicit in the English text. Finally, this dissertation extends the framework to identify the cases that need input text validation, and provide the relevant feedback to the user to modify the input text for the extraction of correct rules. This research also demonstrates the extensibility of the framework. Specifically, the framework was initially developed using the subset of a manufacturing handbook only including milling, metal stamping, and die-casting sections, and then applied to the rest of the manufacturing processes including 30 sections in forming, machining, casting, molding, assembling, and finishing chapters in the book. Case studies are performed to demonstrate the feasibility of the framework on the dataset of 133 sentences. First, the feasibility of the rule extraction framework is shown by extracting correct rules from approx. 57% of the sentences. Second, the effectiveness of ambiguity resolution by complementing standard NLP techniques with manufacturing domain knowledge is demonstrated by an increasing the correct rules to 70%. Lastly, for the remaining 30% of the cases that need input text validation, relevant feedback is provided to the user to modify the input text for the extraction of the correct rules. It is expected that this research will facilitate the development of formal manufacturing knowledge including complex manufacturing rules. It will thus address an important barrier that has prevented a larger scale application and the adoption of semantic technologies in the field of manufacturing, especially for semantics-based manufacturability analysis.","abstract_has_math":false,"creators":["Kang, Sungku"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Dutta, Debasish","Ferreira, Placid","Kim, Harrison Hyung Min","Patil, Lalit","Rangarajan, Arvind"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:25:30Z","date_published":"2018-03-13T15:25:30Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Rule extraction","Semantic technology","Natural language processing (NLP)","Ontology"],"languages":["en"],"rights":["Copyright 2017 SungKu Kang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99242","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dutta, Debasish","Ferreira, Placid","Kim, Harrison Hyung Min","Patil, Lalit","Rangarajan, Arvind"]},{"key":"dc:creator","label":"Author","values":["Kang, Sungku"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:25:30Z","2020-03-14T09:15:16Z","2017-12-06","2017-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Rule extraction","Semantic technology","Natural language processing (NLP)","Ontology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 SungKu Kang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99242"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Semantics-based approaches—founded on the idea of explicitly encoding meaning separately from the data or the application code—are being applied to manufacturing, for example, to enable early manufacturability feedback. These approaches rely on formal, i.e., computer-interpretable, knowledge and rules along with the context or semantics. On the other hand, manufacturing knowledge has been maintained primarily in the form of unstructured English text. It is considered impractical for engineers to author accurate, formal, and structured manufacturing rules. Previous efforts on extracting semantics from unstructured text in manufacturing have focused exclusively on basic concept names and hierarchies. In this context, this dissertation focuses on the development of a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a formal form, from unstructured English text such as those written in manufacturing handbooks. This dissertation includes the following specific research tasks. First, it studies the problem in manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. Second, it extends the framework to complement standard Natural Language Processing (NLP) techniques with manufacturing domain knowledge to resolve ambiguities, called as domain-specific ambiguities, that are due to manufacturing-specific meanings implicit in the English text. Finally, this dissertation extends the framework to identify the cases that need input text validation, and provide the relevant feedback to the user to modify the input text for the extraction of correct rules. This research also demonstrates the extensibility of the framework. Specifically, the framework was initially developed using the subset of a manufacturing handbook only including milling, metal stamping, and die-casting sections, and then applied to the rest of the manufacturing processes including 30 sections in forming, machining, casting, molding, assembling, and finishing chapters in the book. Case studies are performed to demonstrate the feasibility of the framework on the dataset of 133 sentences. First, the feasibility of the rule extraction framework is shown by extracting correct rules from approx. 57% of the sentences. Second, the effectiveness of ambiguity resolution by complementing standard NLP techniques with manufacturing domain knowledge is demonstrated by an increasing the correct rules to 70%. Lastly, for the remaining 30% of the cases that need input text validation, relevant feedback is provided to the user to modify the input text for the extraction of the correct rules. It is expected that this research will facilitate the development of formal manufacturing knowledge including complex manufacturing rules. It will thus address an important barrier that has prevented a larger scale application and the adoption of semantic technologies in the field of manufacturing, especially for semantics-based manufacturability analysis.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Sungku Kang, accepted the attached license on 2017-12-06 at 10:30.","The student, Sungku Kang, submitted this Dissertation for approval on 2017-12-06 at 12:06.","This Dissertation was approved for publication on 2017-12-06 at 13:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11880 on 2018-03-13 at 09:57:11","Made available in DSpace on 2018-03-13T15:25:30Z (GMT). No. of bitstreams: 3 KANG-DISSERTATION-2017.pdf: 10590363 bytes, checksum: 5598a9b15079b994b08515fdc4248449 (MD5) LICENSE.txt: 4208 bytes, checksum: 347e9b230281778873c68294d16510c6 (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: c55d67ee034da6c44b39413b1d48ce7e (MD5) Previous issue date: 2017-12-06","Embargo set by: Seth Robbins for item 105205 Lift date: 2020-03-13T15:25:40Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 105205 Lift date: 2020-03-13T15:28:52Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 105205 on 2020-03-14T09:15:16Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Extraction of formal manufacturing rules from unstructured English text"]}]}],"canonical_facts":{"dc:contributor":["Dutta, Debasish","Ferreira, Placid","Kim, Harrison Hyung Min","Patil, Lalit","Rangarajan, Arvind"],"dc:creator":["Kang, Sungku"],"dc:date":["2018-03-13T15:25:30Z","2020-03-14T09:15:16Z","2017-12-06","2017-12"],"dc:description":["Semantics-based approaches—founded on the idea of explicitly encoding meaning separately from the data or the application code—are being applied to manufacturing, for example, to enable early manufacturability feedback. These approaches rely on formal, i.e., computer-interpretable, knowledge and rules along with the context or semantics. On the other hand, manufacturing knowledge has been maintained primarily in the form of unstructured English text. It is considered impractical for engineers to author accurate, formal, and structured manufacturing rules. Previous efforts on extracting semantics from unstructured text in manufacturing have focused exclusively on basic concept names and hierarchies. In this context, this dissertation focuses on the development of a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a formal form, from unstructured English text such as those written in manufacturing handbooks. This dissertation includes the following specific research tasks. First, it studies the problem in manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. Second, it extends the framework to complement standard Natural Language Processing (NLP) techniques with manufacturing domain knowledge to resolve ambiguities, called as domain-specific ambiguities, that are due to manufacturing-specific meanings implicit in the English text. Finally, this dissertation extends the framework to identify the cases that need input text validation, and provide the relevant feedback to the user to modify the input text for the extraction of correct rules. This research also demonstrates the extensibility of the framework. Specifically, the framework was initially developed using the subset of a manufacturing handbook only including milling, metal stamping, and die-casting sections, and then applied to the rest of the manufacturing processes including 30 sections in forming, machining, casting, molding, assembling, and finishing chapters in the book. Case studies are performed to demonstrate the feasibility of the framework on the dataset of 133 sentences. First, the feasibility of the rule extraction framework is shown by extracting correct rules from approx. 57% of the sentences. Second, the effectiveness of ambiguity resolution by complementing standard NLP techniques with manufacturing domain knowledge is demonstrated by an increasing the correct rules to 70%. Lastly, for the remaining 30% of the cases that need input text validation, relevant feedback is provided to the user to modify the input text for the extraction of the correct rules. It is expected that this research will facilitate the development of formal manufacturing knowledge including complex manufacturing rules. It will thus address an important barrier that has prevented a larger scale application and the adoption of semantic technologies in the field of manufacturing, especially for semantics-based manufacturability analysis.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Sungku Kang, accepted the attached license on 2017-12-06 at 10:30.","The student, Sungku Kang, submitted this Dissertation for approval on 2017-12-06 at 12:06.","This Dissertation was approved for publication on 2017-12-06 at 13:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11880 on 2018-03-13 at 09:57:11","Made available in DSpace on 2018-03-13T15:25:30Z (GMT). No. of bitstreams: 3 KANG-DISSERTATION-2017.pdf: 10590363 bytes, checksum: 5598a9b15079b994b08515fdc4248449 (MD5) LICENSE.txt: 4208 bytes, checksum: 347e9b230281778873c68294d16510c6 (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: c55d67ee034da6c44b39413b1d48ce7e (MD5) Previous issue date: 2017-12-06","Embargo set by: Seth Robbins for item 105205 Lift date: 2020-03-13T15:25:40Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 105205 Lift date: 2020-03-13T15:28:52Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 105205 on 2020-03-14T09:15:16Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/99242"],"dc:language":["en"],"dc:rights":["Copyright 2017 SungKu Kang"],"dc:subject":["Rule extraction","Semantic technology","Natural language processing (NLP)","Ontology"],"dc:title":["Extraction of formal manufacturing rules from unstructured English text"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:37Z"}