Abstract
This paper introduces a strategy for both the retrieval and analysis of linked open data (LOD) based on the use of visual tools. Retrieving and understanding data from triplestores (such as SPARQL) requires technical knowledge and proves to be challenging with large datasets, which result in an increase in the mental overload when unknown ontologies are involved in the creation of complex queries. These two problems benefit greatly from visual techniques that allow for executing them in an easier and more intuitive manner. These techniques have already been applied to each problem separately; however, we propose combining them to lower the complexity of triple-store data retrieval and empower its exploration and analysis through specific data visualizations. To demonstrate the suitability of this strategy, a web-based tool was implemented. It allows for the creation of interactive queries using node-link ontology representations, and a subsequent filtering and analysis through a configurable dashboard with different data visualizations. This paper also presents a user-centred design process and evaluation of the proposed tool.
Highlights
Visualization is a key human capability for understanding information [1]
While the majority of approaches have focussed on either the visualization of ontologies or the underlying linked open data (LOD), methods offering a holistic view of semantically-enabled datasets by combining these two aspects aspects are scarce
We focus on the exploration of data exposed through SPARQL2 endpoints and the asserted ontology where the relationships are defined
Summary
Visualization is a key human capability for understanding information [1]. Visual representations are aimed at leveraging the user’s understanding of datasets. We conceive the filtering of data as an interactive task done once the user has retrieved it and is able to explore it, rather than a previous step done in the database query This decision leads to a shorter and more intuitive initial phase where queries only require an entity and attribute selection. The present article covers both the workflow and its implementation as an interactive tool in the following order: Section II discusses previous work in ontology visualization, RDF data visual exploration and dashboard usage in the field.
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