# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 6 Discussion and Concluding Remarks  
A review of both research literature and industrial practices concerning ontologies shows that there are no quantitative measures or KPIs to evaluate and compare the results and effectiveness of industrial ontologies. This is one of the major limitations of ontology development studies and a potential subject for further studies. For this reason, in this section, we compare our study with the most pertinent literature. We consider various key features of design and development, such as the main principles adopted for ontology design, whether the ontology was tested in an industrial use case or in the development of a software platform, the focus of the ontology, and whether any method was followed when designing the ontology. In this paper, we followed the design principles of the BFO and used a two-cycle methodology for semantic model design, as explained in detail in Section 4. The focus of our ontology is maintenance in the manufacturing context, and an industrial test was performed to validate the ontology that also aided the successful implementation of the Z-BRE4K platform [50]. The most pertinent research to compare to our study in terms of achievements are by [PERSON] et al. (2022) [51], [PERSON] et al. (2021) [52], [PERSON] et al. (2021) [34], [PERSON] et al. (2020) [53], [PERSON] et al. (2019) [54], and [PERSON] et al. (2019) [40]. The method we followed for the development of the semantic model--the use of a two-cycle methodology, competency questions, and BFO compliance--is the strength of our model. Table 1 highlights the achievements of our research in comparison with other relevant literature. This paper reviewed the data-federation and data-management challenges of obsolete systems and proposed the use of semantic technology for Industry 4.0. Inspired by the competency questions, the two-cycle methodology was introduced to create the domain ontology. It comprises a vocabulary cycle and a context cycle to strengthen semantics through interactive updates. The maintenance ontology was created through the two-cycle methodology. This ontology was designed considering the use together with other domain ontologies, following BFO principles. This ontology was tested and verified on the Z-BRE4K platform with KBS. As semantic services are provided by the KBS, the maintenance ontology was used for data governance as part of the platform. This article discusses interoperability at semantic and technical levels. Other key contributions were removing ambiguous terms and redundant contextual terminologies, focusing on established vocabularies, and emphasizing a structure to compartmentalize essential vocabulary. This approach could be suitable for broader adoption; however, this falls beyond the scope of this study. Leveraging the maintenance ontology and the encapsulation of maintenance domain knowledge helps to achieve semantic enrichment for the manufacturing industry. Future work could utilize the proposed method in other relevant manufacturing practices, such as agile manufacturing, additive manufacturing, information integration in cloud manufacturing, and digital manufacturing. Semantic interoperability in industry remains a challenge. To successfully adopt Industrial Ontologies Foundry (IOF) ontologies and adapt them to different industries, a real-world application and associated validation are crucial. The proposed ontology was verified on the stated platforms; however, the validation of this methodology on large datasets remains a topic for future research. To overcome this limiting factor in future studies and align with the vision of the current work, a large-scale testing environment could promote learning for researchers who develop and propose ontologies and for customers who use ontologies in their industries. On a more complex level, research could be conducted in industries with different geographical locations, diverse cultures, and a variety of organizational issues. This could help highlight the importance of data integration and harmonization.