Semantic enrichment of product data supported by machine learning techniques

Ruben Costa, Paulo Figueiras, Ricardo Jardim-Goncalves, Jose Ramos-Filho, Celson Lima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The process of transforming big data into understandable information is the key of sustainable innovation within an Industry 4.0 factory. Machine learning techniques and cyber-physical systems are closely related to realize a new thinking of production management and factory transformation. Textual data collected in machinery logs or product documentation, does not exhibit a rich structure which can be easily understandable by both humans and machines. Therefore, data in an unstructured format needs to be enriched and transformed into a representation schema that exhibits a higher degree of structure, before it can be used and shared. The paper, introduces a novel conceptual framework to create knowledge representations from unstructured data sources, based on enriched Semantic Vectors, using a classical vector space model extended with ontological support. Hence, this research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (i.e., semantic associations) modelled by domain ontologies with the addition of information presented in documents, addresses the challenges concerning data exchange and its understanding within Industry 4.0 scenarios, when supported by semantic technologies. The proposed approach is validated with industrial examples of product data used in the building and construction domain (e.g., technical specifications concerning climate control, electric power and lighting products) showing its benefits in a real-world use case.

Original languageEnglish
Title of host publication2017 International Conference on Engineering, Technology and Innovation
Subtitle of host publicationEngineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1472-1479
Number of pages8
Volume2018-January
ISBN (Electronic)9781538607749
DOIs
Publication statusPublished - 2 Feb 2018
Event23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017 - Madeira Island, Portugal
Duration: 27 Jun 201729 Jun 2017

Conference

Conference23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017
CountryPortugal
CityMadeira Island
Period27/06/1729/06/17

Fingerprint

Learning systems
Semantics
Knowledge representation
Industrial plants
Climate control
Electronic data interchange
Vector spaces
Machinery
Ontology
Industry
Innovation
Lighting
Specifications
Cyber Physical System
Big data

Keywords

  • Big data
  • machine learning
  • ontologies
  • product data
  • semantic interoperability

Cite this

Costa, R., Figueiras, P., Jardim-Goncalves, R., Ramos-Filho, J., & Lima, C. (2018). Semantic enrichment of product data supported by machine learning techniques. In 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings (Vol. 2018-January, pp. 1472-1479). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICE.2017.8280056
Costa, Ruben ; Figueiras, Paulo ; Jardim-Goncalves, Ricardo ; Ramos-Filho, Jose ; Lima, Celson. / Semantic enrichment of product data supported by machine learning techniques. 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1472-1479
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Costa, R, Figueiras, P, Jardim-Goncalves, R, Ramos-Filho, J & Lima, C 2018, Semantic enrichment of product data supported by machine learning techniques. in 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1472-1479, 23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017, Madeira Island, Portugal, 27/06/17. https://doi.org/10.1109/ICE.2017.8280056

Semantic enrichment of product data supported by machine learning techniques. / Costa, Ruben; Figueiras, Paulo; Jardim-Goncalves, Ricardo; Ramos-Filho, Jose; Lima, Celson.

2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1472-1479.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The process of transforming big data into understandable information is the key of sustainable innovation within an Industry 4.0 factory. Machine learning techniques and cyber-physical systems are closely related to realize a new thinking of production management and factory transformation. Textual data collected in machinery logs or product documentation, does not exhibit a rich structure which can be easily understandable by both humans and machines. Therefore, data in an unstructured format needs to be enriched and transformed into a representation schema that exhibits a higher degree of structure, before it can be used and shared. The paper, introduces a novel conceptual framework to create knowledge representations from unstructured data sources, based on enriched Semantic Vectors, using a classical vector space model extended with ontological support. Hence, this research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (i.e., semantic associations) modelled by domain ontologies with the addition of information presented in documents, addresses the challenges concerning data exchange and its understanding within Industry 4.0 scenarios, when supported by semantic technologies. The proposed approach is validated with industrial examples of product data used in the building and construction domain (e.g., technical specifications concerning climate control, electric power and lighting products) showing its benefits in a real-world use case.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Costa R, Figueiras P, Jardim-Goncalves R, Ramos-Filho J, Lima C. Semantic enrichment of product data supported by machine learning techniques. In 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1472-1479 https://doi.org/10.1109/ICE.2017.8280056