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Facilitating the Exploration and Visualization of Linked Data

Abstract. The creation and the improvement of tools that cover exploratory and visualization tasks for Linked Data were one of the major goals focused in the LOD2 project. Tools that support those tasks are regarded as essential for the Web of Data, since they can act as a useroriented starting point for data customers. During the project, several development efforts were made, whose results either facilitate the exploration and visualization directly (such as OntoWiki, the Pivot Browser) or can be used to support such tasks. In this chapter we present the three selected solutions rsine, CubeViz and Facete.

1 Introduction

The increasing number of datasets that are available as Linked Data on the Web makes it difficult for dataset curators to review additions, removals or updates of assertions involving resources they authored. Existing approaches like central registries do not scale with the fast-changing nature of the Web, thus being outdated or incomplete. In this chapter we propose a set of approaches that deal with the exploration and visualization of Linked Data. First we present the Resource SubscrIption and Notification sErvice (rsine) in Sect. 2 which enables subscribers to register for notifications whenever changes to RDF datasets occur. Thereby, we outline our approach based on a controlled vocabulary development scenario and integrate it into two exemplary LOD2 stack components to show its applicability. Based on requirements that come from practical experience in thesaurus development at Wolters Kluwer Germany, we describe how rsine can be used to check and avoid introduction of potential thesaurus quality problems. Secondly, we showcase in Sect. 3 CubeViz, a flexible exploration and visualization platform for statistical data represented adhering to the RDF Data Cube vocabulary. If statistical data is represented according to that vocabulary, CubeViz exhibits a faceted browsing widget allowing to interactively filter observations to be visualized in charts. Based on the selected structural part, CubeViz offers suitable chart types and options for configuring the visualization by users. We present the CubeViz visualization architecture and components, sketch its underlying API and the libraries used to generate the desired output.

By employing advanced introspection, analysis and visualization bootstrapping techniques CubeViz hides the schema complexity of the encoded data in order to support a user-friendly exploration experience.

Lastly, we present Facete in Sect. 4, which is an application tailored for the exploration of SPARQL-accessible spatial data. Facete is built from a set of newly developed, highly modular and re-usable components, which power the following features:

advanced faceted search with support of inverse properties and nested properties;

automatic detection of property paths that connect the resources that matches the facet selection with those resources that can be shown on the map; and

a map component that operates directly on a SPARQL endpoint and automatically adopts its data retrieval strategy based on the amount of available spatial information.

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