## 2 Background  
Modern complex systems often face tasks of resource distribution, including knowledge [9]. At present, the most effective means of knowledge distribution is the ontological model [11]. It should be noted that, in many of these works, the distributed ontology is not fully formalized, which does not allow us to abstract from the individual cases and solve the tasks of building datasets and knowledge in a general way. Using the distributed ontologies is advisable only when really enormous volumes of heterogeneous information exist [12]. The most characteristic feature of complex systems, including integrated ones, is decentralization and a low level of unification [11; 18; 19]. There is no technical possibility of the simultaneous processing of all system information, and it is also impossible to implement universal algorithms of knowledge processing. The information space of complex systems is not sufficiently normalized due to the qualitative and quantitative content. This space is characterized by linguistic variables that expand the space to a fuzzy one [12; 13; 14; 3; 12]. The ontology is a formal representation of reality; it requires the clear delivery of information, but reality cannot always be clearly expressed, so it is recommended to use fuzzy ontology [11].  
The subject of ontology is the study of the categories of things that are or can exist in a particular subject area [4]. The ontology product is a catalog of types of things that are believed to exist in a certain area \(D\) for a person who uses the language \(L\) to describe \(D\). Types of ontology are entities, meanings of words, concepts, and the relations of the language \(L\) to the aspect of description \(D\)[19]. Uninterpreted logics, such as the calculus of predicates [5] and conceptual graphs [6], are ontologically neutral. They do not affect the subject or how it can be interpreted. Logic itself means nothing, but the combination of logic with ontology generates a language that can express the relationship between objects in a certain subject area [7].  
The existing applied solutions regarding the distributed ontologies are divided into two large groups:  
1. The development of ontologies;
2. The use of ontologies.  
The first group includes the development environments, including visualization environments [4], automatic integration mechanisms [13], and version control systems [14], the qualitative review of which was carried out in [5]. Using the first type involves incomplete ontology, which restricts the use of the semantic component. The second group includes information systems based on ontologies [8] and the mechanisms of logical conclusion [15; 16]. The second group of solutions can fully use the semantic load of the ontology without restrictions.  
During the development of ontologies, it is necessary to take into account the following:  
* The informal ontology can be specified by a set of types, which are defined using natural language;
* The formal ontology is specified by a set of concept names and relations, which are partially ordered using the \"type-subtype\" relation [11].  
The principal tasks that are solved during the creation of systems based on distributed ontologies are the solution of conflict in the case of a conflict and the increase in the completeness and accuracy of information searching in the ontological structure.  
One of the important questions relevant to the ontological structures of complex systems is the logical conclusion based on inadequate input data due to such reasons as [15]:  
* The impossibility of the constant functioning of all the subsystems which contain knowledge;
* An insufficient amount of time resources needed to obtain all the input data;
* An insufficient amount of computing resources needed to process all the input data;
* The incompleteness of the input data in the knowledge base.