# A Knowledge Graph Framework for Dementia Research Data
###### Abstract
Thus, the choice often hinges on the specific project requirements and constraints. In addition to choosing a graph paradigm, selecting a data model or graph schema is another critical decision for building a KG. This model dictates how entities of interest and their relationships are represented within the KG. This aspect can be approached in two main ways: using an ad hoc data model tailored to the project's specific needs or adopting a standard model such as ontologies. In particular, biomedical ontologies have emerged as essential tools in standardizing terminology, modeling biological realities [19], supporting data annotation [20; 21; 22; 23], and facilitating biomedical text mining [24; 25]. With ongoing concerted efforts from the scientific community, these ontologies have evolved to incorporate fine-grained knowledge across various biomedical subdomains, as exemplified by initiatives such as the Open Biological and Biomedical Ontologies (OBO) Foundry [26] and the National Center for Biomedical Ontology (N CBO) [27] and its BioPortal [28]. Moreover, using logical modeling and annotation, biomedical ontologies make assertions that span and connect levels of biological organization, from the molecular level to phenotype and disease definitions. This ability to traverse and link multiple scales of biological information makes ontologies an invaluable resource for the construction of KGs for biomedical research. The biomedical field is rich in open databases that offer scientific knowledge from various subdomains, including molecular biology (genomics, proteomics, and pathways), drugs, and disease characterization. These sources hold the potential for a more comprehensive understanding of biomedical phenomena; however, their value is often hindered by their dispersal across different platforms. KGs have emerged as instrumental tools for integrating and exploiting these disparate sources, fostering a multitude of projects that aim to unify the spread-out biomedical knowledge. A prime example of such an initiative is the Monarch Initiative [29], which integrates genetic, phenotypic, and disease-related data to facilitate the identification of disease genes and variants. Similarly, the Clinical Knowledge Graph (CKG) [30] is an open-source platform that integrates proteomics, public databases, and literature. It effectively utilizes KGs to augment and enrich biomedical data, thereby facilitating informed clinical decision-making. Likewise, PrimeKG [31] is a multimodal KG that integrates a multitude of high-quality resources, representing various biological scales, i.e., from genotypes to clinical phenotypes. The scalable precision medicine open knowledge engine (SPOKE) [32] also integrates multiple biological data sources to provide structured knowledge ranging from low-level molecular biology to pharmacology and clinical practice. Furthermore, the KG-COVID-19 [33] project responded to the COVID-19 crisis by building a unified KG from disparate biomedical information about SARS-CoV-2, illustrating how KGs can effectively drive knowledge synthesis, particularly in emergent health situations. As the number of available KGs increases, it has become evident that social and technical limitations exist, especially the need for standardization in entity naming and graph representation approaches [34; 35]. Regarding modeling standardization, the Biolink Model [36] has emerged as a high-level data model that provides standard terms and relations for describing biological entities and their relationships for organizing data in biomedical KGs. Biolink serves both as a map for bringing together data from different sources under one unified model and as a bridge between ontological domains. As a similar initiative to OBO, centered around KGs, the KG-Hub project [37] provides a collection of tools and libraries for building interoperable KGs and a mechanism for sharing them to foster their reuse.