DAta-CEntric AI-driven Data Linking – DACE-DL

In a world of open science and FAIR data, Linked Data has been gaining popularity, due to the means they offer for (meta)data exchange, federation and sharing on the Web. Data linking is the scientific challenge of automatically establishing typed links between the entities of two or more structured datasets. A variety of complex data linking systems exists, evaluated on public benchmarks. While they have allowed for the generation of vast amounts of linked data in the context of various dedicated projects, data generic systems often have limited applicability in many real-world scenarios, where data are highly heterogeneous and domain-specific. DACE-DL targets a paradigm shift in the data linking field with a data-centric bottom-up methodology relying on machine learning and representation learning models. Based on current and previous linked data projects (in music, encyclopedia, agronomy, biodiversity), we will study what a hybrid AI approach can learn from already available and diverse linked data and modularized existing data linking systems in order to re-inject this knowledge into current Web-data challenges, for example in the context of the COVID-19 crisis or in the agronomy domain.