Radon – Rapid Discovery of Topological Relations

Authors

  • Mohamed Sherif University of Leipzig
  • Kevin Dreßler University of Leipzig
  • Panayiotis Smeros Swiss Federal Institute of Technology in Lausanne (EPFL)
  • Axel-Cyrille Ngonga Ngomo University of Leipzig

DOI:

https://doi.org/10.1609/aaai.v31i1.10478

Keywords:

Link Discovery, Topological relation, Optimization, Linked Data

Abstract

Geospatial data is at the core of the Semantic Web, of which the largest knowledge base contains more than 30 billions facts. Reasoning on these large amounts of geospatial data requires efficient methods for the computation of links between the resources contained in these knowledge bases. In this paper, we present Radon – efficient solution for the discovery of topological relations between geospatial resources according to the DE9-IM standard. Our evaluation shows that we outperform the state of the art significantly and by several orders of magnitude.

AAAI-2017 Proceedings Cover

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Published

2017-02-10

How to Cite

Sherif, M., Dreßler, K., Smeros, P., & Ngonga Ngomo, A.-C. (2017). Radon – Rapid Discovery of Topological Relations. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10478

Issue

Vol. 31 No. 1 (2017): Thirty-First AAAI Conference on Artificial Intelligence

Section

AAAI Technical Track: AI and the Web