# Modularization Method to Reuse Medical Knowledge Graphs
## 2 Modularization of Knowledge Graphs
Finally, the authors outlined evaluation criteria and requirements: localized semantics, correct reasoning, transitivity, safe reuse, and decidability. In [PERSON] et al. [11], the authors presented the Minimum Information to Reference an External ontology Term (MIREOT) guidelines to aid the development of the ontology of Biomedical Investigations (OBI). The purpose of this guide is to import the minimum required reference from an external ontology; for this the authors propose three cases, depending on what is required as a reference. All of them generally require that the identifier or URI (currently IRI) of the class or term reference be included. The method described in this article agrees to keep the unique identifier of each concept or class, but includes more relevant details of each concept, avoiding an overload in the memory of the reasoner. The main disadvantage of modularization approaches such as MIREOT's is that they seek to import all references to classes that depend on or are semantically related to maintain logical consistency. In the method that we present in this article, the logical consistency is guaranteed by the transformation process used, which depends on a very simple design of classes and semantic relations, and that reuse refers only to concepts. One of the main tradeoffs of ontology modularization is to determine the level of expressiveness that needs to be maintained from the original ontology. However, the higher the level of expressiveness, the lower the ability to reason with large ontologies, and the efficiency will be very poor. Regarding this problem, [PERSON] et al. [12] introduced the Ontology Analysis and Partitioning Tool (OAPT), a framework for analyzing and partitioning ontologies. However, the main approach used by the authors was to partition an ontology into modules and then apply evaluation criteria. Instead, in this article we focus on the extraction of specific information or knowledge that needs to be included in another knowledge graph for a particular purpose. The original graphs are not partitioned into modules. In 2012, [PERSON] et al. [13] presented use cases for the modular development of ontologies by using the import mechanism of OWL. According to the authors, ontology modularization concerns two topics: module extraction, which consists of separating existing ontologies into modules; and modular development, which resembles modular software engineering. In this article we present a modularization method that combines aspects of both cases; on the one hand, the general design of the knowledge graph is carried out with a modular development approach from the beginning, and on the other hand, we present a semi-automatic method to perform the extraction of modules for reuse. [PERSON] et al. [14] described an algorithm to evaluate OBO (Open Biological ontology) ontologies. As a result, they identified a prevalence of unsatisfiable class axioms, and encountered that the axioms belong to the BFO (Basic Formal ontology), which is the upper ontology that most OBO ontologies adhere to. The authors concluded that the BFO is not used consistently among all the OBO ontologies that implement it. Taking this into consideration, our modularization and reuse approach completely avoids importing the entire ontologies and the references they include from the BFO. No inconsistencies will be produced by the reuse of the knowledge graph modules produced by our method. In 2021 [PERSON], [PERSON], and [PERSON] [15] described the reasons why ontologies are not frequently reused as follows: differing representational granularity, lack of conceptual clarity in many ontologies, lack and difficulty of adherence to good modeling principles, and lack of support in ontology engineering tools. Another relevant issue that we deal with in the modularization method described in this paper is related to the way biomedical ontologies are developed, most of which do not define individuals or instances at the assertional level (A-Box); they are mainly designed at the terminological level (T-Box).