# Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs
## 2 Review of Related Works  
Currently, the research and development of knowledge graphs (KG) as models for complex technical objects are performed in various subject areas [5; 6; 7; 8; 9]. The models of monitoring objects, based on KG, and methods for their building in case of telecommunication network monitoring, are considered in [10; 11; 12].  
Solutions for predicting the state of complex objects using knowledge graph models are also presented in a number of papers. For example, the problem of price trend prediction, based on KG and machine learning, was considered in [13]. Further, event prediction that is based on the analysis of the evolutionary knowledge of the event ontology is described in [14]. The variety of subject areas that are \"fit\" to solve the prediction tasks based on KG demonstrates the effectiveness of this technology in solving separate problems of prediction [13; 14; 15] and providing recommendations [16; 17; 18; 19; 20; 21]. However, existing solutions are poorly applicable in regards to solving forecasting problems and providing recommendations when dealing with complex objects with dynamically changing structures. For these objects, the complexity of building models and predicting their states multiplies; moreover, constant restructuring of models is required, as shown in [4; 22]. In [3; 23], the authors proposed building models for complex objects with dynamically changing structures, by developing methods for multilevel object model synthesis. This theory has been widely used in practice. However, the models and methods of multilevel synthesis of KGs were neither considered within the scope of the theory itself, nor within the scope of ongoing applied research. The problems in regards to predicting the state of complex objects with dynamic structures were not defined or solved.  
The research results, aimed at analyzing the possibilities of using the context in the building models of complex systems, have been reflected in a significant number of works. For example, general approaches to the use of context events in modeling objects are presented in [24; 25; 26; 27]. The listed works demonstrate the effectiveness of systems based on content analysis and adaptation of algorithms depending on context events, in comparison to traditional systems that do not use context data. These systems make it possible to achieve better quality indicators, especially in conditions of a priori uncertainty. The results of these works can be used to solve the problem of adaptive synthesis of the knowledge graph of complex monitoring objects.  
In [28], application of the results of the context analysis in the synthesis of object models was proposed--to be carried out using a process control mechanism, using policies and related rules for changing the states and behavior of objects. When developing a method for adaptive synthesis of KGs, it is advisable to use the proposed solutions for context analysis and policy application together.  
The current level of modern KG technologies development and RDF storages allows them to be used in modeling of complex technical systems. A number of works are devoted to the study of systems based on KG performance. A comparative analysis of the developed KGs for solving various problems is presented in the framework of the Smart City Service project [29]. This study shows that the use of KG and RDF data warehouses is suitable for big data solutions. Optimization of the KG size and redesign of SPARQL queries are considered the main approaches to ensure the required performance. This study shows how SPARQL query structures and KG size affect system performance. The relationship between SPARQL query design and system performance has also been analyzed in the Berlin SPARQL Test [30]. The information provided in the above works allows us to assert that KG-based systems can be used in fairly large industrial systems.