iKNOW – Leveraging Knowledge Graphs for iDiv and Biodiversity

Description

Knowledge Graphs (KGs) are widely regarded as one of the most promising ways to manage and link information in the age of Big Data. KGs are an excellent tool for data integration. They can be queried using SPARQL, a powerful query language. Furthermore, new knowledge can be inferred, using, e.g., machine learning or data mining. While KGs are without doubt very useful, creating and maintaining them is not trivial.

The aim of iKNOW is to provide reproducible and reusable workflows for KG creation. This makes it easier to create KGs and also ease their update. By making transparent how a KG was created, they would increase trust in the information provided. In particular, we aim to develop a semantic toolbox for KG creation and evolution and to showcase its usefulness using major iDiv data sources. iKNOW will leverage these data sources to quickly showcase interesting KGs to iDiv scientists and will use them as the foundation for the toolbox.  The overarching research question of iKNOW is How can knowledge graph creation be made FAIR based on transparent, reproducible, and reusable workflows? On the practical side, the proposal aims to leverage the developed approach to increase the visibility and usability of iDiv data.