An Unsupervised Framework for Topological Relations Extraction from Geographic Documents

Corrado Loglisci 1 Dino Ienco 2, 3 Mathieu Roche 4 Maguelonne Teisseire 2, 3 Donato Malerba 1
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we face the problem of extracting spatial relationships from geographical entities mentioned in textual documents. This is part of a research project which aims at geo-referencing document contents, hence making the realization of a Geographical Information Retrieval system possible. The driving factor of this research is the huge amount of Web documents which mention geographic places and relate them spatially. Several approaches have been proposed for the extraction of spatial relationships. However, they all assume the availability of either a large set of manually annotated documents or complex hand-crafted rules. In both cases, a rather tedious and time-consuming activity is required by domain experts. We propose an alternative approach based on the combined use of both a spatial ontology, which defines the topological relationships (classes) to be identified within text, and a nearest-prototype classifier, which helps to recognize instances of the topological relationships. This approach is unsupervised, so it does not need annotated data. Moreover, it is based on an ontology, which prevents the hand-crafting of ad hoc rules. Experimental results on real datasets show the viability of this approach.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00723574
Contributor : Mathieu Roche <>
Submitted on : Friday, August 10, 2012 - 8:56:25 PM
Last modification on : Wednesday, September 18, 2019 - 4:04:04 PM

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Corrado Loglisci, Dino Ienco, Mathieu Roche, Maguelonne Teisseire, Donato Malerba. An Unsupervised Framework for Topological Relations Extraction from Geographic Documents. DEXA: Database and Expert Systems Applications, 2012, Vienna, Austria. pp.48-55, ⟨10.1007/978-3-642-32597-7_5⟩. ⟨lirmm-00723574⟩

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