Visualizing the Drift of Linked Open Data Using Self-Organizing Maps

The urge for evolving the Web into a globally shared dataspace has turned the Linked Open Data (LOD) cloud into a massive platform containing 100 billion machine-readable statements. Several factors hamper a historical study of the evolution of the LOD cloud, and hence forecasting its future: its ever-growing scale, which makes a global analysis difficult; its Web-distributed nature, which challenges the analysis of its data; and the scarcity of regular and time-stamped archival dumps. Recently, a scalable implementation of self-organizing maps (SOM) has been developed to visualize the local topology of high-dimensional data. We use this methodology to address scalability issues, and the Dynamic Linked Data Observatory, a regular biweekly, centralized sample of the LOD cloud, as a time-stamped collection. We visualize the drift of Linked Datasets between 2012 and 2016, finding that datasets with high availability, high vocabulary reuse, and modeling with commonly used terms in the LOD cloud are better traceable across time