# Modularization Method to Reuse Medical Knowledge Graphs
## 1 Introduction  
Knowledge graphs in the medical area have become enormously relevant to support medical research development and to facilitate the exchange of clinical data and scientific advances. That is the case with the well-known NCBO BioPortal, a medical ontology repository where multiple health institutions publish their ontologies or knowledge graphs. In this article, we describe a modularization-based method for the reutilization of portions of medical knowledge graphs into a general model, maintaining reasoning efficiency. Regarding the modularization of knowledge graphs, this topic has been widely studied from the perspective of Ontological engineering. Therefore, we consider the works developed for the modularization of ontologies as applicable to knowledge graphs. However, it is important to clarify the definitions of ontology and knowledge graphs. The term ontology in Computing can be traced back to the 1990s, when authors such as [PERSON] [1], [PERSON] [2] and [PERSON] [3] presented formal definitions and discussions regarding possible interpretations. Taking these definitions as a reference, in this paper we consider that an ontology consists of the following elements and requirement:  
1. A set of concepts or classes, which may be referred to as a vocabulary of terms or conceptualization. 2. A set of semantic relations between concepts, which may be taxonomic or hierarchical relationships, and general relationships between concepts (not taxonomic). 3. A set of individuals or instances. The set of elements mentioned above must satisfy the requirement of _formality_, that is, they must be defined by logical axioms in such a way that misinterpretations of the definitions are avoided. On the other hand, the concept of knowledge graphs emerged more recently and was popularized by Google; since 2012 it has gained great relevance derived from the large number of knowledge graphs publicly available, in addition to the ease of access to them through query languages such as SPARQL or through REST APIs. However, the formal concept of knowledge graphs is still vague; for example, [PERSON] and [PERSON] [4] present an analysis of various definitions. [PERSON] et al. [5] revised few definitions and stated that the following interpretations are possible:  
2. Knowledge graphs are used to: 1. [label=()]
3. describe _real world entities_ and their relationships organized as graphs;2. define classes and relations of entities in a scheme; 3. allow any entity to be related to other entities; 4. they cover various domains of knowledge or topics. Taking into consideration the mentioned references, we can observe that there is a generalized notion that the term knowledge graph refers to real data instances (in fact, a knowledge graph can refer to a large amount of data or resources), while ontologies have a high formalization requirement. Another difference is the type of representation or syntax used; in ontologies, a description logic-based syntax is mainly used, while in knowledge graphs a triplet-based language such as RDF or Turtle is used. In this article, we take as a reference the related works of modularization applied to ontologies and we consider them as relevant to propose a method of reusing knowledge graphs through modules. This is despite the fact that knowledge graphs and ontologies have differences; in practice it is possible to transform an ontology based on description logic syntax into a knowledge graph. In fact, the set of knowledge graphs used in this article are referred as biomedical ontologies; however, we are using the triplet-based representation, specifically RDF. Reutilization of knowledge graphs occurs during the design and integration of multiple semantic representation models (ontologies or knowledge graphs) with the aim of generating a complete representation model to solve the requirements of intelligent information systems or expert systems. Reutilization of a knowledge graph or ontology is the task of completely or partially importing one model into another.