Life sciences knowledge graph construction with more semantics and less code
Creators: name: Ammar, Ammar
Publisher: name: Zenodo
Year: 2024

description: Research generates a massive amount of data on a daily basis. In contrast, a knowledge graph is a graph-structured data model that stands out in integrating heterogeneous data and depicting connections between its entities. Large volumes of data can greatly benefit from knowledge graphs, which streamline data organization and facilitate efficient searching and querying. Linked data, the basis of the semantic web, represents a valuable design approach to construct knowledge graphs, as it not only embraces open standards but also contributes to data FAIRness.

To go from datasets in common formats to linked data knowledge graphs, a series of steps (i.e. workflow) need to be performed, called an ETL workflow (Extract, Transform, Load). Moreover, different technical skills are needed to carry on this process. For example, knowing how to read, parse and manipulate different data formats like tables, JSON, relational databases and APIs (i.e extraction). Then, the programming skills to transform and convert the data into linked data (i.e transformation). Finally, the technical knowledge of triple stores to store and query the knowledge graph (i.e load). Therefore, the steep learning curve and the diverse set of coding skills and tools to create knowledge graphs pose a major barrier to the wider adoption of such an approach by the scientific community. To address this challenge, this tutorial will walk the participants through the process of constructing semantic knowledge graphs from scratch using LinkedPipes ETL. LinkedPipes is a browser-based tool that provides a visual, drag-and-drop interface for creating ETL workflows that integrate heterogeneous data sources into a linked data knowledge graph. It cuts down the necessary amount of coding and uses SPARQL for transformation which is the same language used to query the resulting knowledge graph.

This tutorial begins with introducing SPARQL and RDF, guiding participants from basics to advanced SPARQL usage. Next, it will demonstrate how to get started with the portable environment for building and querying knowledge graphs using Docker. This containerized setup simplifies technical complexities of installing and setting up the individual tools. Next, we’ll discuss a data model for bioassay data (e.g. gene expression microarrays, bioactivity assays) including different entity types like: genes, proteins, pathways, drugs, and dataset provenance (DCAT and VoID vocabularies). We’ll emphasize the importance of data provenance for enhancing FAIRness. Using the semantic model, we’ll convert an input dataset to RDF, extend it with external sources like UniProt, WikiPathways, and ChEMBL, and load the resulting knowledge graph into a triple store. Lastly, participants will learn to write SPARQL queries to answer bio-chemical questions, with examples showcasing the power of knowledge graphs in data integration and modeling.

This work is funded by the eScience Center Fellowship 2023, awarded to the main facilitator of the tutorial: Ammar Ammar

https://www.esciencecenter.nl/fellowship-programme/ammar-ammar/