Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis
Jenifer Tabita Ciuciu-Kiss,
Daniel Garijo
Abstract:Science Knowledge Graphs (SKGs) have emerged as a means to represent and capture research outputs (papers, datasets, software, etc.) and their relationships in a machine-readable manner. However, different SKGs use different taxonomies, making it challenging to understand their overlaps, gaps and differences. In this paper, we propose a quantitative bottom-up analysis to assess the overlap between two SKGs, based on the type annotations of their instances. We implement our methodology by assessing the category… Show more
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