# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 3 Related Works  
[PERSON] et al. present one of the earlier works, from a major industrial company on capturing industrial information models using W3C standards [8]. This work proposes an application front-end to allow non-semantic experts to develop ontologies. The front-end is a modified Web-Protege [9], that hides the complexity associated with the desktop Protege version. The work highlights the benefits of involving domain experts to capture the domain knowledge and to create different services using it. However, the solution does not cover our main requirements. [PERSON] et al. [10] propose a generic online tool for building collaborative ontology without prior deep knowledge of the domain. An initial ontology is built, populated and enriched by multiple participants in a collaborated manner. The goal is to simplify the ontology based modeling of domain knowledge for the users without ontology expertise. However, the ontology concepts are not sufficiently abstracted, as these concepts are simply renamed. This means that despite simplification, substantial semantic expertise is needed to make the necessary modifications to the ontology. Another important missing element is that there are no checks to ensure consistency which can be an issue in a multiple user environment. There is a large pool of work on industrial maintenance. [PERSON] et al. [11] created a taxonomy of the Prognostics and Health Management in manufacturing. They propose a formal ontology for failure prognostics based on industrial ISO standards for failure mode analysis, failure diagnostics and prognostics (e.g., ISO 13372, ISO 13379, ISO 13381 and others). Failure knowledge is described in ontologies from ISO standards. Semantic Web Rule Language (SWRL) [12] is used to define rules in order to generate warning messages in case of abnormal states. However no approach is described to share the acquired failure knowledge among different users. In [13], [PERSON] et al. used ontologies for fault diagnosis for industrial control applications. They utilized reasoning capability to check the model consistency over the time and to raise early-alarms for critical failures. [PERSON] et al. [14] propose an ontology based support for fault diagnosis for aircraft maintenance operations. An aircraft maintenance ontology is modeled and fed by the (manual) alignment of several existing ontologies related to the avionics domain to discover the relations between the causes and failure symptoms, explain the failures and any unscheduled maintenance requirements as well as the possible procedures that can be applied to each situation. An ontology-based approach is adopted by [PERSON] et al. [15] for the fault diagnostics of conveyors. The knowledge about fault symptoms, fault causes and fault solutions was modeled with multiple ontologies. The resulted ontologies were mapped together based on a mathematical formulation of conveyor fault diagnostics. Some reasoning rules were defined in order to infer additional relations between faults, symptoms and potential causes. In [16], [PERSON] et al. used ontologies to model the knowledge of fault diagnosis for rotating machines. Their proposed ontology model describes fault diagnosis knowledge considering the vibration characteristics as main fault factor. The model's reasoning capability is considered by defining some SWRL rules for fault diagnostics. [PERSON] et al. [17] also relied on ontologies to design a loader fault diagnosis system. It aims to help users find the fault causes, locations and fault maintenance measures of loaders in a reasonable amount of time. Ontology is used to model the loader information and describe the relative failures. This work uses the Condition Based Reasoning (CBR) method to diagnose loader faults by finding similar corresponding situations in the past. When no corresponding case is found, CBR fails and the (SWRL-based) Rule Based Reasoning (RBR) approach is proposed for fault diagnosis. In [18], [PERSON] et al.