# A Knowledge Graph Framework for Dementia Research Data
## 2 Related Work  
Advancements in storage and graph technologies, coupled with the increasing availability of open scientific data, have led to the emergence of multiple biological KGs [49]. Projects such as the Monarch Integrated Knowledge Graph, the Clinical Knowledge Graph(CKG), PrimeKG, and the scalable precision medicine open knowledge engine (SPOKE), previously introduced in the introduction, bear similarities to our initiative.  
The Monarch Integrated Knowledge Graph [29] is a notable example of biological KGs, which assimilates various data types (including genotype, phenotype, and disease) from multiple sources into a unified semantic graph model. The Monarch KG has been instrumental in our project, DemKG, as it not only serves as a primary data source but also offers an array of tools we utilize. Our philosophy aligns closely with that of the Monarch KG, emphasizing a robust semantic foundation while integrating data from a variety of external sources, including other ontologies and extensions. We build upon this work to extend it with dementia-related knowledge and provide means for integrating study data.  
CKG [30] is an open-source platform designed to harmonize a wide range of \"omics\" data types into a coherent structure, including genomics, transcriptomics, proteomics, and metabolomics. CKG favors a custom data model formed from a selected set of concepts and relationships from specific ontologies. On top of the KG, CKG integrates statistical and machine learning algorithms to streamline the analysis and interpretation of typical proteomics workflows. DemKG resonates with CKG's mission to improve the modeling and integration of omics data. However, it deviates fundamentally from its approach to data modeling, wherein CKG employs a more circumscribed model.  
PrimeKG [31] is a multimodal KG for precision medicine analyses. Like its counterparts, it integrates a plethora of resources to describe a broad spectrum of diseases with relationships across major biological scales. One of them is combining the entire range of approved drugs with their therapeutic action, distinguishing it from other systems. Moreover, unlike DemKG, PrimeKG employs a custom approach to its data model, incorporating ten types of nodes and thirty types of undirected edges extracted from reference ontologies. Furthermore, it lacks a systematic schema to integrate experimental and study data.  
SPOKE [32] is a KG that connects information from 41 biological data sources, structured as 21 different node types and 55 edge types, ranging from low-level molecular biology to pharmacology and clinical practice. It uses 11 different ontologies to organize the data semantically meaningfully and, in its last iteration, also integrates the Biolink model whenever it is found to be practical. SPOKE is implemented as a Neo4j database built from a collection of Python scripts and provides a graphical user interface and a REST API for end-user access. Our method stands distinct from SPOKE in several crucial aspects. Primarily, it offers an open toolkit for KG construction and personalization, ensuring both platform and representational paradigm autonomy. Moreover, despite utilizing a comparable modeling approach, DemKG fosters a closer connection with a vast array of domain ontologies by preserving links to explicitly defined terms and relationships. Finally, our framework provides a flexible and robust module for research data integration.  
In summary, our work distinguishes itself from similar efforts through a comprehensive approach that integrates a well-established terminological foundation and community standards, follows design patterns conducive to data integration, and defines terminological extensions specific to the dementia domain, facilitated through a dedicated low-code solution for seamless study data integration.