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PUBLICATIONS

A Thesaurus-guided Framework for Visualization of Unstructured Manufacturing Capability Data

Published

Author(s)

Farhad Ameri, William Z. Bernstein

Abstract

Manufacturing companies advertise their manufacturing capabilities and services on their company website using natural language text. The unstructured capability data published on the web is a rich source of formal and informal manufacturing terms and concepts. Through systematic mining of a large collection of capability text, new semantic models and knowledge patterns can be extracted that can be used as the steppingstone of more formal ontologies. The objective of this research is to develop a framework for better understanding, analyzing, and summarizing manufacturing capability data that is available on the websites of manufacturing companies. The findings can support supply chain decisions and also may result in discovery of new trends and associativity patterns in the data. The particular focus of this paper is on demonstrating how visual analytics (VA) tools can be used for visualization and summarization of manufacturing capability information. A visual analytics system, named Jigsaw, is used for exploring the connections between various entities such as process, material, industry, and equipment across the documents in the experimental dataset.
Proceedings Title
APMS 2017 International Conference Advances in Production Management Systems (APMS 2017)
Conference Dates
September 3-7, 2017
Conference Location
Hamburg, DE
Pub Type
Conferences

Download Paper

https://doi.org/10.1007/978-3-319-66923-6_24
Local Download

Keywords

manufacturing capability, thesaurus, visual analytics, Jigsaw
Visualization research, Systems integration, Supply chain and Manufacturing systems design and analysis

Citation

Ameri, F. and Bernstein, W. (2017), A Thesaurus-guided Framework for Visualization of Unstructured Manufacturing Capability Data, APMS 2017 International Conference Advances in Production Management Systems (APMS 2017), Hamburg, DE, [online], https://doi.org/10.1007/978-3-319-66923-6_24, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923484 (Accessed April 26, 2024)

Created August 30, 2017, Updated October 12, 2021