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Exploring LOD through metadata extraction and data-driven visualizations

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Purpose

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

Design/methodology/approach

– 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.

Findings

– 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.

Research limitations/implications

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

Originality/value

– 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.
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Keywords: Datatype inference; Exploratory data analysis; LOD visualization; Linked Open Data; Metadata extraction; Semantic Web

Document Type: Research Article

Affiliations: University of Deusto, Bilbao, Spain

Publication date: July 4, 2016

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