# Modularization Method to Reuse Medical Knowledge Graphs
## 2 Modularization of Knowledge Graphs  
One of the first attempts to address the modularization of representation models was reported by [PERSON] in 2003 [6], who stated the importance of modularity as a key requirement for ontologies to achieve reutilization, maintainability, and evolution. Modularization of ontologies was defined as the task of decomposing an ontology into independent disjoint skeleton taxonomies. In 2006, [PERSON] and [PERSON] [7] described a modularization approach aimed at supporting the distribution of knowledge in a P2P network. The authors describe the requirements of reasoning efficiency, robustness, and maintainability and proposed the following structural criteria: connectedness of modules (the efficiency of distributed computation is closely related with the degree of interconnectedness of the generated modules); size and number of modules (this criteria has a strong impact on the robustness); and redundancy of representation. (the use of redundant representations will improve robustness at the cost of increasing maintenance). The goal of this approach is to apply modularization on a given ontology with the purpose of splitting it by considering numerical aspects such as size, number of modules, and module interconnectedness. From a logic-based perspective, a series of contributions have been reported by a research group led by [PERSON] and [PERSON]. In 2006 [8], [PERSON] et al. described the notion of a module from a model-theoretic perspective as a self-contained unit within the ontology. They stressed additional module requirements such as the scope of the module, its size, and the correct interpretation of the module. Later, in 2007 [9], [PERSON] et al. presented an approach to extracting modular fragments from ontologies, preserving the minimal conditions. They defined a module with respect to a signature \(S\); aimed at reducing the cost of importing external ontologies, they proposed to import only a fragment of the given ontologies, preserving the meaning of the terms imported. Based on the aforementioned references, a module can be defined as a self-contained knowledge unit within the knowledge graph, where a module has a clearly defined scope, size, and a correct interpretation of the module. Meanwhile modularization can be defined as the task of decomposing a knowledge graph into independent disjoint modules, splitting it considering modules size, number of modules, and module interconnectedness. Accordingly, knowledge graph modularization should address the following criteria:  
1. Facilitate reutilization and the evolution of knowledge graphs. 2. Maintain reasoning efficiency. 3. Keep the balance between robustness and maintainability. 4. Enable distribution of knowledge in open networks. 5. Support connectedness of modules. The concepts of module and modularization are closely related to the process of reusing knowledge graphs, because the purpose is to extract modules from knowledge graphs so that they can be imported into another model, ensuring that the semantic definitions and relations are not lost while maintaining the efficiency of reasoning. Therefore, in the rest of this article we will use the concept of the Knowledge Graph Module (KGM) as the resulting product of the modularization process. In 2009, [PERSON] et al. [10] presented a survey on modular ontology techniques that are based on logical formalisms and graph theories. They state that modular ontology techniques are crucial for the biomedical domain, because most popular ontologies are large and complex. Therefore, the development of tools for managing multiple distributed ontologies will benefit reasoning performance. The authors establish that an ontology module should be self-contained and logically consistent. Also defined were the goals of ontology modularization: partial reuse, complexity, ownership and customization, efficient reasoning, and tooling support.