Visual Analytics System for LOD Using Sampling-based Structure Estimation

Abstract

This paper proposes information visualization system for exploratory LOD (Linked Open Data) Analysis. The LOD is a framework to make data open to the public. Recently, it has been widely used to publish various kinds of data such as statistical data, geographical data, and academic data. The RDF (Resource Description Framework), which describes data as a set of triples consisting of subject, predicate and object, is commonly used to publish data as LOD. When we want to use LOD, it is necessary to understand its structure, such as graph structure of RDF data, used vocabularies and resources. Therefore, we often have to conduct exploratory analysis of LOD. In order to support the analysis, the proposed system analyzes the structure of LOD written with RDF, and visualizes the result of analysis. As most of currently available LOD have table structure, the proposed system identifies whether target dataset contains table structure or not using resource sampling with SPARQL queries. Graph structure of resources obtained by the resource sampling is visualized by assigning different colors to different tables. A user cannot only examine the visualized structure, but also conduct exploratory search by selecting a resource in the visualized result. Effectiveness of the proposed system is evaluated by applying it to several LOD resources. Experiments with test participants are also conducted, of which results show even users who are not familiar with RDF can perform exploratory analysis effectively.