![]() ![]() Methodology, the Linked Open Terms (LOT) methodology. In this thesis, we propose a guideline booklet in which we recommend an existing Result in a challenging and time-consuming task. However, as they do not know how to formalise knowledge, it could That role does not exist in a particular organisation, and DEs try to build ontologies It may also be that KEs are unavailable or Thus, DEs may rely, for the most part, on KEsįor creating ontologies, while these lack proper expertise regarding the specific domain,Ĭreating a cycle of knowledge dependency. Ontology development exist, but there is no single user-friendly guidebook or template Indeed, to obtain reasonable and optimal knowledge modelling,īoth groups of actors, Domain Experts (DEs) and Knowledge Engineers (KEs), shouldĬollaborate and complement each other’s work. Real-time data related to the production system, including processes and users’ data.įor achieving this, it is necessary to have semantic models (ontologies), i.e., a formalĭescription of the knowledge related to, in this case, IoP and particularly to the Its goal is to enable a new way of data understanding by integrating semantics in ![]() The Internet of Production (IoP) is one of the clusters of excellence at RWTH University. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. ![]() The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). ![]() Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). ![]()
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