[GHE17] Visualizing Large-scale Linked Data with Memo Graph

Conférence Internationale avec comité de lecture : 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2017), September 2017, pp.10, Marseille, france,

Mots clés: Linked Data , Visualization

Résumé: Many studies, in the literature, have affirmed a low level of user satisfaction concerning the understandability and readability of large-scale Linked Data visualizations offered by current available tools. This issue is especially problematic for inexperienced users. To address these requirements, we have extended our previous work Memo Graph, an ontology visualization tool, to provide a user-centered interactive solution for extracting and visualizing Linked Data. It takes aim to provide comprehensible and legible visualization. To manage scalability, it is built on an incremental approach to extract descriptive summarization from a given Linked Data endpoint where it becomes possible to generate a “summary graph” from the most important data (middle-out navigation approach). It offers user interfaces that reduce task complexity for users, especially the inexperienced ones. We tested Memo Graph on a number of Linked Data datasets with encouraging results. We discuss the promising results derived from an empirical evaluation, which affirmed that Memo Graph is useful in visualizing Linked Data and usable.

Collaboration: Sfax


@inproceedings {
title="{Visualizing Large-scale Linked Data with Memo Graph}",
author=" F. Ghorbel and F. Hamdi and N. Ellouze and E. Metais and F. Gargouri ",
booktitle="{21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2017)}",
address="Marseille, france",