An ontology-based framework for knowledge-based lifecycle management of product information. Hands-on on a proof-of-concept implementation. | Zendy

Lorenzo Failla | Zendy; Marco Rossoni | Zendy; Giorgio Colombo | Zendy

Open Access

An ontology-based framework for knowledge-based lifecycle management of product information. Hands-on on a proof-of-concept implementation.

Author(s) -

Lorenzo Failla,

Marco Rossoni,

Giorgio Colombo

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3621327

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Significant challenges persist in contemporary Product Lifecycle Management (PLM) implementations, particularly in the areas of knowledge management and information interoperability. These challenges are especially evident in the context of complex, highly customized products that are managed within Engineer-to-Order (ETO) industrial settings. Prior research has suggested that ontologies may offer a promising solution to address these gaps. This paper assesses the effectiveness of a general and agnostic ontology model in addressing the persistent challenges in knowledge management and enhancing information interoperability in the landscape of such products. The evaluation is supported by a detailed physical implementation, which formalizes the domain knowledge of a case study product through 101 classes, 39 predicates, and 65 rules, generating a 45000-axioms inferred model of 172 interrelated individuals. The obtained model is then validated against typical knowledge management scenarios derived from ETO industrial insights. The outcomes of this study underscore the importance of converging on a universally accepted, ontology-based, and standardized approach for shaping the foundation of the next-generation PLM systems.

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