Efficient Representation of Very Large Linked Datasets as Graphs

Maria Krommyda, Verena Kantere, Yannis Vassiliou

Abstract

Large linked datasets are nowadays available on many scientific topics of interest and offer invaluable knowledge. These datasets are of interest to a wide audience, people with limited or no knowledge about the Semantic Web, that want to explore and analyse this information in a user-friendly way. Aiming to support such usage, systems have been developed that support such exploration they impose however many limitations as they provide to users access to a limited part of the input dataset either by aggregating information or by exploiting data formats, such as hierarchies. As more linked datasets are becoming available and more people are interested to explore them, it is imperative to provide an user-friendly way to access and explore diverse and very large datasets in an intuitive way, as graphs. We present here an off-line pre-processing technique, divided in three phases, that can transform any linked dataset, independently of size and characteristics to one continuous graph in the two-dimensional space. We store the spatial information of the graph, add the needed indices and provide the graphical information through a dedicated API to support the exploration of the information. Finally, we conduct an experimental analysis to show that our technique can process and represent as one continuous graph large and diverse datasets.

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Paper Citation


in Harvard Style

Krommyda M., Kantere V. and Vassiliou Y. (2020). Efficient Representation of Very Large Linked Datasets as Graphs.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 106-115. DOI: 10.5220/0009389001060115


in Bibtex Style

@conference{iceis20,
author={Maria Krommyda and Verena Kantere and Yannis Vassiliou},
title={Efficient Representation of Very Large Linked Datasets as Graphs},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={106-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009389001060115},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Efficient Representation of Very Large Linked Datasets as Graphs
SN - 978-989-758-423-7
AU - Krommyda M.
AU - Kantere V.
AU - Vassiliou Y.
PY - 2020
SP - 106
EP - 115
DO - 10.5220/0009389001060115