Discovering Spatial and Temporal Links among RDF Data
Panayiotis Smeros, Manolis Koubarakis · 2016
Link Discovery is a new research area of the Semantic Web which studies the problem of nding semantically related entities lying in dierent knowledge bases. This area has become more crucial recently, as the volume of the available Linked Data on the web has been increasing considerably. Although many link discovery tools have been developed, none of them takes into consideration the discovery of spatial or temporal relations, leaving datasets with such characteristics weakly interlinked and therefore disallowing the exploitation of the rich information they provide. In this paper, we propose new methods for Spatial and Temporal Link Discovery and provide the rst implementation of our techniques based on the well-known framework Silk. Silk, enhanced with the new features, allows data publishers to generate a wide variety of spatial, temporal and spatiotemporal relations between their data and other Linked Open Data, dealing eectively with the common heterogeneity issues of such data. Furthermore, we experimentally evaluate our implementation by using it in a real-world scenario and demonstrate that it discovers accurately all the existing links in a time ecient and scalable way.