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.

LDViz: A Tool to Assist the Multidimensional Exploration of SPARQL Endpoints

“…Visualization per Datatype. In an attempt to improve the visualization of LOD by considering the characteristics of the data, a few tools have been proposed to analyze the RDF vocabulary of the input data to visualize it accordingly (e.g., data containing properties such as xsd:date and ical:dtstart would be visualized through timeline or calendar visualizations) [3,27,32]. Similarly, the S-Paths visualization tool [13] supports the visualization of resources sets based on semantic paths by identifying and ranking a set of visualization techniques suitable to explore the data.…”

A methodology to automatically translate user requirements into visualizations: Experimental validation

“…In [13] a framework is proposed that chooses the best type of visualization. Similarly, in [14] some visualization types are related to those types of users objectives that could be more compliant with. Finally, the SkyViz approach asks users to specify a structured visualization context and determines the suitable types of visualization [4].…”

Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue

“…Examples of relevant factors include: the type of source, the role of users and their expertise, the goal of the analytic process, and the frequency with which the data can, or must, be analyzed. In this research direction, approaches have been, or continue to be, proposed to automate data visualization from user requirements [18,62,44]. The Model Driven Architecture (MDA) approach automates the derivation of the most appropriate visualization from user requirements [27].…”