The Human Face of the Web of Data: A Cross-sectional Study of Labels
Lucie-Aimée Kaffee, Elena Paslaru Bontas Simperl · Procedia Computer Science · 2018
Labels in the web of data are the key element for humans to access the data. We introduce a framework to measure the coverage of information with labels. The framework is based on a set of metrics including completeness, unambiguity, multilinguality, labeled object usage, and monolingual islands. We apply this framework on seven diverse datasets, from the web of data, a collaborative knowledge base, open governmental and GLAM data. We gain an insight into the current state of labels and multilinguality on the web of data. Comparing a set of differently sourced datasets can help data publishers to understand what they can improve and what other ways of collecting and data can be adopted.