# A Knowledge Graph Framework for Dementia Research Data
###### Abstract  
Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer's disease biomarkers. knowledge graphs; ontologies; graph databases; data modeling; dementia; omics +
Footnote †: journal: Medical  
complicated by the enormous diversity and complexity of individual diseases. These hurdles highlight the need for innovative strategies and tools to harness the potential of multi-modal data in propelling the field of precision medicine. Since biological reality is often modeled as a network or graph [8; 9], one technological approach that has gained significant traction is the use of knowledge graphs (KGs) [10], which allow for the integration and organization of diverse biomedical data types, facilitating their analysis and interpretation. After Google introduced the knowledge graph in 2012, highlighting the advantages of the approach [11], KGs have become increasingly popular, finding adoption in industry with subsequent launches by companies such as Microsoft, Amazon, Airbnb, and Facebook [12], as well as in academia [13; 14]. Nonetheless, the definition of KGs can vary based on the application context. In biomedicine, they can be characterized as data structures meant to gather and disseminate real-world knowledge, where nodes depict significant biomedical entities and the edges delineate diverse relationships that could exist between these entities [15]. KGs embody a methodological transition toward a more comprehensive representation of reality, facilitating the integration of heterogeneous data types and providing an intuitive, graph-based structure for representing intricate relationships between diverse biomedical entities. Constructing a KG entails a series of methodological and technological decisions that profoundly impact the utility and effectiveness of the resulting product. A pivotal consideration in this process is the selection of a graph paradigm, which provides the theoretical and practical foundation for the structure and function of the KG. There are two primary approaches in this regard: Resource Description Framework (RDF) and Labeled Property Graphs [16; 17; 18]. Both of these approaches offer robust technological solutions, but each has its own strengths and weaknesses. While RDF offers standardization and robustness ideal for semantic applications, it may suffer from verbosity and computational inefficiency. Conversely, LPGs excel in their flexibility and intuitive structure, which allow for the straightforward representation of complex relationships and properties on both nodes and edges, but they may struggle in scenarios demanding high interoperability and standardization.