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.
Highlights
These days ontology modeling has become the standard de facto for knowledge and data representation, especially for applications in complex domains, where unstructured or semistructured data are the main source of information
We show how the proposed ontology can be applied as a reference model for the data representation level for the creation of a knowledge graph in a weakly formalized domain
The knowledge bases are not used for decision-making. Without modeling instruments such as the proposed IIP CRM it is practically impossible to formalize, represent and explain different individual processes related to some particular agricultural subject
Summary
These days ontology modeling has become the standard de facto for knowledge and data representation, especially for applications in complex domains, where unstructured or semistructured data are the main source of information. CPM-based models allow the explicit capture explicitly and semantic inheritance of run-time process management These models are still rigid with modeling user needs and IoT environment. A reference model [...] does seek to provide a common semantics that can be used unambiguously across and between different implementations.” [3] In other words, such model is a domain-specific ontology that facilitates communication and development processes. Development of a semantic reference model of process individualization will allows the building of specific knowledge graphs for more flexible and adaptive information process management in IoT;. Development of a proper CRM logical scheme to automatically link the levels of data representation in IoT systems and the levels of description of user needs considering their business requirements expressed in domain-specific terms;. Ahlemann in his work considers several models starting from one of the first reference information models for project management in the architecture, engineering, and construction (AEC) industry published by Froese, who called it a “standard model” [10] and later developments such as a combined reference information model for process and project controlling published by Schlagheck [11]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call