Enhancing factory data integration through the development of an ontology: from the reference models reuse to the semantic conversion of the legacy models

Abstract

Data integration is one of the most crucial challenges for current manufacturing companies. Indeed, while the extended use of software tools generate more and more data about products, processes, and production resources, still this huge amount of data is represented using different formats and non-aligned structures. This issue is worsened by the fact that data can be found scattered in not linked databases and hosted in mutually incompatible systems. The growing need to access these data and the knowledge that they encapsulate on a global view from different perspectives is addressed by various approaches in order to support the integration between involved systems. This paper deals with the applicability of Semantic Web technologies in industrial context to enhance semantic interoperability. In particular, it proposes a systematic approach to support the development of a semantic model, focusing on the combination of two main critical aspects: the reuse of existing reference models and the semantic migration of the existing legacy models. Even both the aspects have been separately studied in previous research works and even their overlap is recurring during the stage of ontology development, to the best of our knowledge the literature is missing studies covering the synergistic and automatic combination of these two aspects within a holistic approach. The application possibilities of the approach are also investigated within a real case study from a high-precision mould-making company; thus demonstrating its feasibility for use in complex manufacturing contexts.

Keywords:

• data integration
• interoperability
• semantic web
• knowledge reuse
• legacy models

Acknowledgement

The research reported in this paper has been funded by the European Union 7th FP (FP7/2007–2013) under the grant agreement No: 314156, Engineering Apps for advanced Manufacturing Engineering (Apps4aME), and by the Italian research project Smart Manufacturing 2020 within the Cluster Tecnologico Nazionale Fabbrica Intelligente [CTN01_00163_216744].

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://www.tanet.eu/projects/current-projects/flexinet/.

2. http://www.w3.org/TR/owl-time.

3. http://protege.stanford.edu/.

4. https://www.w3.org/2001/sw/wiki/Pellet.

Additional information

Funding

This work was supported by the Seventh Framework Programme [314156] and by the Italian Cluster Tecnologico Nazionale Fabbrica Intelligente [CTN01_00163_216744].