Semantic Reference Model for Individualization of Information Processes in IoT Heterogeneous Environment

Abstract

The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and machine learning. The combination of both AI techniques imposes several requirements and restrictions on the types of data and object properties and the structure of ontologies for data and knowledge representation about processes. The conceptual reference model for effective individualization of information processes (IIP CRM) proposed in this work considers these requirements and restrictions. This model is based on such well-known standard upper ontologies as BFO, GFO and MASON. Evaluation of the proposed model is done on a practical use case in the field of precise agriculture where IoT-enabled processes are widely used. It is shown that IIP CRM allows the construction of a knowledge graph about processes that are surrounded by unstructured data in soft and heterogeneous domains. CRM also provides the ability to answer specific questions in the domain using queries written with the CRM vocabulary, which makes it easier to develop applications based on knowledge graphs.