Manufacturing companies maintain manufacturing knowledge primarily as unstructured text. To facilitate formal use of such knowledge, previous efforts have utilized natural language processing (NLP) to classify manufacturing documents or extract manufacturing concepts/relations. However, extracting more complex knowledge, such as manufacturing rules, has been evasive due to the lack of methods to resolve ambiguities. Specifically, standard NLP techniques do not address domain-specific ambiguities that are due to manufacturing-specific meanings implicit in the text. To address this important gap, we propose an ambiguity resolution method that utilizes domain ontology as the mechanism to incorporate the domain context. We demonstrate its feasibility by extending our previously implemented manufacturing rule extraction framework. The effectiveness of the method is demonstrated by resolving all the domain-specific ambiguities in the dataset and an improvement in correct detection of rules to 70% (increased by about 13%). We expect that this work will contribute to the adoption of semantics-based technology in manufacturing field, by enabling the extraction of precise formal knowledge from text.
Ontology-Based Ambiguity Resolution of Manufacturing Text for Formal Rule Extraction
Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received March 21, 2018; final manuscript received November 20, 2018; published online February 4, 2019. Assoc. Editor: Ying Liu.
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Kang, S., Patil, L., Rangarajan, A., Moitra, A., Robinson, D., Jia, T., and Dutta, D. (February 4, 2019). "Ontology-Based Ambiguity Resolution of Manufacturing Text for Formal Rule Extraction." ASME. J. Comput. Inf. Sci. Eng. June 2019; 19(2): 021003. https://doi.org/10.1115/1.4042104
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