Link discovery is central to the integration and use of data across RDF knowledge bases. Geospatial information is increasingly represented according to the Linked Data principles. Resources within such datasets are described by means of vector geometry, where link discovery approaches have to deal with millions of point sets consisting of billions of points. In this paper, we study the effect of simplifying the resources? geometries on runtime and F-measure of link discovery approaches. In particular, we evaluate link discovery approaches for computing the point-set distances as well as the topological relations among RDF resources with geospatial representation. The results obtained on two different real datasets suggest that most geospatial link discovery approaches achieve up to 67� speedup using simplification, while the average loss in their F-measure is less than 15\%. Our implementation is open-source and available at http://github.com/dice-group/limes.
%0 Conference Paper
%1 GeoSimp2018
%A Ahmed, Abdullah Fathi
%A Sherif, Mohamed Ahmed
%A Ngomo, Axel-Cyrille Ngonga
%B SEMANTiCS 2018 - Research Track
%D 2018
%K 2018 abdullah dice group_aksw limes ngonga projecthobbit sage sherif simba slipo
%T On the Effect of Geometries Simplification on Geo-spatial Link Discovery
%U http://svn.aksw.org/papers/2018/SEMANTICS_GeoSimp/paper/public.pdf
%X Link discovery is central to the integration and use of data across RDF knowledge bases. Geospatial information is increasingly represented according to the Linked Data principles. Resources within such datasets are described by means of vector geometry, where link discovery approaches have to deal with millions of point sets consisting of billions of points. In this paper, we study the effect of simplifying the resources? geometries on runtime and F-measure of link discovery approaches. In particular, we evaluate link discovery approaches for computing the point-set distances as well as the topological relations among RDF resources with geospatial representation. The results obtained on two different real datasets suggest that most geospatial link discovery approaches achieve up to 67� speedup using simplification, while the average loss in their F-measure is less than 15\%. Our implementation is open-source and available at http://github.com/dice-group/limes.
@inproceedings{GeoSimp2018,
abstract = {Link discovery is central to the integration and use of data across RDF knowledge bases. Geospatial information is increasingly represented according to the Linked Data principles. Resources within such datasets are described by means of vector geometry, where link discovery approaches have to deal with millions of point sets consisting of billions of points. In this paper, we study the effect of simplifying the resources? geometries on runtime and F-measure of link discovery approaches. In particular, we evaluate link discovery approaches for computing the point-set distances as well as the topological relations among RDF resources with geospatial representation. The results obtained on two different real datasets suggest that most geospatial link discovery approaches achieve up to 67� speedup using simplification, while the average loss in their F-measure is less than 15\%. Our implementation is open-source and available at http://github.com/dice-group/limes.},
added-at = {2024-06-18T09:44:08.000+0200},
author = {Ahmed, Abdullah Fathi and Sherif, Mohamed Ahmed and Ngomo, Axel-Cyrille Ngonga},
biburl = {https://www.bibsonomy.org/bibtex/282f7259025945f0cd0cdb588a3c64320/aksw},
booktitle = {SEMANTiCS 2018 - Research Track},
interhash = {6d7b08ed9f75a6ccbc1840c7b185307b},
intrahash = {82f7259025945f0cd0cdb588a3c64320},
keywords = {2018 abdullah dice group_aksw limes ngonga projecthobbit sage sherif simba slipo},
month = sep,
series = {SEMANTiCS '18},
timestamp = {2024-06-18T09:44:08.000+0200},
title = {On the Effect of Geometries Simplification on Geo-spatial Link Discovery},
url = {http://svn.aksw.org/papers/2018/SEMANTICS_GeoSimp/paper/public.pdf},
year = 2018
}