## 2 Related Work
An important aspect in FCA is the fact that a concept is defined as a pair of related object and attribute sets. Thus, every concept is uniquely identified in its context by the corresponding object set or attribute set. Here, we note that the objects in FCA are the atoms in the concept lattice, i.e., they correspond to the individuals in the ontology model. Considering these attributes, we can generally consider them as predicates having true or false Boolean values. In this sense, FCA attributes may correspond to property-value pairs in ontology. Thus, as schematic models highlight the viewpoint that attributes (predicates, properties) are the key elements in identifying the concepts, in FCA the attributes must be first-order members of semantic models. One of the first models integrating ontology and FCA methods was presented in [20]. Their proposed model was based on the following principles:  
* Concepts are described by their properties;
* The concept hierarchy is determined by the properties;
* The same property sets mean the same concepts. The construction of ahn ontology is controlled and supported by a corresponding concept lattice. Every ontology can be mapped to a unique context and FCA concept lattice. The ontology design tool can perform the following supporting actions:  
* Visualization of the related FCA concept lattice;
* If two concepts are assigned to the same property set, an error message can be raised to modify the ontology;After selecting a set of properties, a new related concept is generated, the system determines its position in the taxonomy graph, and the new concept is inserted into the current concept lattice. A similar integration approach was presented in [21] for constructing an ontology to describe the semantics of business actions. The target ontology was constructed in a distributed way by integrating local component descriptions. The input component models describe the relevant properties of the actions, then the integration module coverts them into concepts using the FCA engine. In the proposal presented in [22], an FCA concept lattice based on property relationships was constructed to determine the taxonomy of the ontology classes. Each class was associated with the FCA concept having the largest number of shared properties. Regarding applied cases, one of the first publications was the development of a clinical domain ontology [23]. This ontology was generated from a set of 368 textual patient discharge reports using natural language preprocessing modules to convert texts into terms and attributes. Later, several other application studies on ontology construction with FCA support were worked out, among others, in the fields of tourism [24] and naval operations [25]. Another important approach is the combination of direct rule-based languages with FCA to construct domain ontologies [26]. Rule-based language can be used to enrich an ontology with additional relationships and axioms [27]. Ontology is an actively investigated research domain. Considering recent works, we highlight the following fields:  
* Integration of ontology into smart systems, such as e-tutor systems [28; 29]
* Application of ontology-based tools in schematic modeling [30]
* Integration of ontology and machine learning methods [31]
* Ontology in knowledge mining [32]. Due to the complexity of ontological modeling and to the strong relationship and inference with other semantic modeling tools, there is a need to provide objective measures of the quality of ontological modeling and schema. In the literature, there are works analysing the requirements [33; 34]; however, there is no general and widely accepted theoretical and technical foundation for the synthesis of these requirements. Thus, the development of efficient tools to support modeling of ontologies remains a real and relevant research topic in the knowledge engineering community.