Exploring LOD through metadata extraction and data-driven visualizations

The purpose of this paper is to present a new approach toward automatically visualizing Linked Open Data (LOD) through metadata analysis.

By focussing on the data within a LOD dataset, the authors can infer its structure in a much better way than current approaches, generating more intuitive models to progress toward visual representations.

With no technical knowledge required, focussing on metadata properties from a semantically annotated dataset could lead to automatically generated charts that allow to understand the dataset in an exploratory manner. Through interactive visualizations, users can navigate LOD sources using a natural approach, in order to save time and resources when dealing with an unknown resource for the first time.

This approach is suitable for available SPARQL endpoints and could be extended for resource description framework dumps loaded locally.

Most works dealing with LOD visualization are customized for a specific domain or dataset. This paper proposes a generic approach based on traditional data visualization and exploratory data analysis literature.