# Modularization Method to Reuse Medical Knowledge Graphs
## 8 Conclusions  
One of the difficult problems to solve during the reuse of medical knowledge graphs is that they directly or indirectly import other ontologies or graphs. This generates an overload of models in RAM memory, which prevents reasoning and inference from being carried out more efficiently.  
In this article, we have described a modularization method applicable to large knowledge graphs; in particular, in graphs from the biomedical area that, in addition to being large in volume, are highly complex for the user. The described modularization method differs from methods that seek to divide a graph into parts, each of which is self-contained. It is also different from fully automatic methods that extract a module by considering logically complete modules.  
The modularization method described is semi-automatic and is more oriented towards having support tools to solve very specific needs of information or specialized knowledge that must be incorporated into larger and more complex knowledge graphs.  
The examples of application of the method allow one to observe and analyze the specific decision making that must be performed during the design of the knowledge modules and the selection of the necessary attributes to meet the requirements, and that once the design is complete, a mechanism for automatic population is programmed.  
It should be noted that the reuse of knowledge graphs entails many challenges, one of the most complicated being that the structure of the original graph may not be the most appropriate for the destination graph. Therefore, in the examples shown in this article, we have made the decision to make changes in the way of representing the classes and class hierarchies. We have chosen to handle many concepts or classes as individuals in the destination graph.  
Conceptualization, M.B. and D.G.-V.; formal analysis, M.B. and J.A.R.-O.; investigation, M.B., J.A.R.-O. and L.D.S.-M.; methodology, M.B.; software, D.G.-V.; validation, M.B., J.A.R.-O. and L.D.S.-M.; writing--original draft, M.B., J.A.R.-O. and L.D.S.-M.; writing--review and editing, M.B., D.G.-V., J.A.R.-O. and L.D.S.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The knowledge graphs that were generated with the described method are published and available at: [[http://aisii.azc.uam.mx:8082/HealthKnowledgeGraphs/index.html](http://aisii.azc.uam.mx:8082/HealthKnowledgeGraphs/index.html)]([http://aisii.azc.uam.mx:8082/HealthKnowledgeGraphs/index.html](http://aisii.azc.uam.mx:8082/HealthKnowledgeGraphs/index.html)) (accessed on 15 November 2022).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DOID Original KG Metrics** & **Disease KGM Metrics** \\ \hline Axiom: 179,536 & Axiom: 78,874 \\ Logical axiom count: 29,282 & Logical axiom count: 72,418 \\ Declaration axioms count: 17,994 & Declaration axioms count: 6456 \\ Class count: 17,898 & Class count: 1 \\ Object property count: 45 & Object property count: 0 \\ Data property count: 0 & Data property count: 6 \\ Individual count: 0 & Individual count: 6449 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the original DOID metrics with the Disease KGM obtained.