Exploring LOD through metadata extraction and data-driven visualizations. Issue 3 (4th July 2016)

• Record Type: Journal Article
• Authors: Peña, Oscar; Aguilera, Unai; López-de-Ipiña, Diego
• Is Part Of: Program. Volume 50:Issue 3(2016)
• Page Start: 270
• Page End: 287
• Publication Date: 2016-07-04
• Subjects: Linked Open Data -- Exploratory data analysis -- Semantic Web -- Datatype inference -- LOD visualization -- Metadata extraction; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-12-2015-0079
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : 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.