# A Knowledge Graph Framework for Dementia Research Data
## 4 Results  
We have developed a KG framework that harmonizes biomedical knowledge and evidence from various sources, coupled with a transformation module designed to streamline the integration of multi-modal and omics data in dementia research. The core components of the framework encompass the extensions ontology builder, which provide ontological definitions to fill identified gaps from the domain ontologies; the KG-builder, in charge of obtaining, merging, and producing the KG; and the data transformer module, a low-code interface to transform source study data. All components are publicly accessible on GitHub ([[https://github.com/demkg-framework/](https://github.com/demkg-framework/)]([https://github.com/demkg-framework/](https://github.com/demkg-framework/)), accessed on 30 August 2023). This trio of tools forms an intuitive building pipeline and also offers flexibility for customization, enabling users to construct the graph from scratch, adapt it to specific requirements, and deploy it on their preferred platform and graph database.  
The backbone of our implementation is rooted in established community standards, technologies, and methodologies. The initial step involved the selection of a comprehensive array of domain reference biomedical ontologies, primarily from OBO, to form an expressive knowledge model for our primary KG. These ontologies offer a variety of well-defined concepts across varying levels of granularity, encapsulating intricate details of biological reality in the form of hierarchical relationships and concept networks.  
To facilitate a consistent term mapping across various ontologies and mitigate computational demands, we utilized pre-built KGs from the KG-Hub initiative and the KG-OBO subset as our foundation, employing the KGX tool for the merging phase of the KG-builder pipeline. The KG-Hub initiative utilizes the Biolink model as its high-level data model, which we adopted to introduce greater flexibility and provide a comprehensive yet adaptable terminology overlay on the ontological model. The Biolink model facilitated the creation of both relaxed and detailed modeling and query capabilities, thereby enhancing the standardization and flexibility of our model. The default KG consists of 1.5 M nodes and 11.5 M edges.  
To fill the identified gaps in the foundational model, we developed specific terminological extensions through the extensions ontology. We employed ODK to systematically introduce new terms, leveraging the OBO ecosystem to import and extend relevant external terms using DOS-DP whenever feasible.  
Finally, the transformation module provides a low-code solution to transform tabular source data and generate necessary instance nodes and edges by following specific design patterns that effectively depict study visits, phenotype observations, measurements/analyses derived from samples, and neuropsychological test results. These design patterns promote efficient data instantiation under the ontological model of the source research data, interconnecting various aspects of the study design outputs and providing a robust platform for data querying and network-oriented analyses. Figure 4 shows an overview of the framework components.