# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 2 Motivation for Implementing Semantic Technologies toward Industry 4.0  
During the last two decades, manufacturing problems have become more diverse and complicated [23]. Where the contexts of data in obsolete systems are ambiguous or lost, data-management challenges are as follows [24]:  
* The documents and guidelines depicting the use of datasets may be corrupted or lost. * Often, a data description disaccords with its field name. * It is hard to manage tabular datasets or update their data schemas. Even if a new database or table is created to overcome these challenges, there remains a problem with data mapping. When field names are updated, it is difficult to retrieve data in the previous database or table. Moreover, data instances can have different names because technical terminology depends on the services and functions provided. For instance, global and multicultural companies may face a multilingual information problem, depending on the locations and languages. To solve the data-mapping problem automatically through communication between the old and new datasets, a company should make extra efforts to understand the structure of the data schema and the meaning of the data. Sometimes, it is impossible to understand these aspects with the provided information. Furthermore, depending on the data source, datasets are stored in different formats that need to be reflected in the automatic method. Therefore, creating an automatic process may become a highly demanding task in terms of both time and resources. In this regard, [PERSON] et al. (2020) proposed a graphic-based user interface to help non-experts efficiently interact with data [25]. They employed a SPARQL protocol and resource description framework query language (SPARQL) query to extract surface mount technology (SMT) data using triples. Today, the emergence and advancements of Industry 4.0 technologies are leading to a paradigm shift in manufacturing. Manufacturing applications have been widened, and manufacturing companies are trying to seamlessly connect their manufacturing assets [26]. In addition, their assets are creating big data that captures their behaviors [27]. However, the challenges mentioned above complicate data management since manufacturing companies first need to spend time and resources to perform unwieldy research to comprehend the data schema and formats. Semantic technologies provide certain capabilities to machines and systems. Using these technologies, machines can do more beneficial work, and systems facilitate reliable communication between machines. Semantic technology facilitates the capture of data's meaning. To cope with the challenges mentioned above, semantic technology provides clear and explicit meanings for data instances, an ontology as a semantic model, standard metadata, and reference of semantics. It enables machines to share a common understanding with humans. The ontology provides certain rules to describe data instances and their labels; therefore, following the rules of the ontology, all data instances can have meaning and context. In other words, semantic technology facilitates context-awareness by linking a data instance to a predefined class in an ontology or another instance, or both. The context of data can be clearly and explicitly stated because an ontology provides strong terminology based on natural language. For this reason, it enables convenient data storage and retrieval. It is of paramount importance that an ontology provides standard meanings and rules for data. Particularly in an Industry 4.0 environment, it is necessary for multiple systems to communicate with each other. Generally, each system has its own format to describe data; hence, interoperability is essential and critical to overcoming issues caused by the interaction of a variety of formats. An ontology provides appropriate locations to store data, as it is a graphical representation of domain knowledge, including explicit definitions of terms.