Paper Info
Title
Exploring LOD through metadata extraction and data-driven visualizations.
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.
Year
DOI
Venue
2016
10.1108/PROG-12-2015-0079
PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS
Keywords
DocType
Volume
Linked Open Data,Exploratory data analysis,Semantic Web,Datatype inference,LOD visualization,Metadata extraction
Journal
50
Issue
ISSN
Citations 
3
0033-0337
2
PageRank 
References 
Authors
0.40
11
3
Name
Order
Citations
PageRank
Oscar Peña1113.60
Unai Aguilera2416.97
Diego López-de-Ipiña322751.47