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Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents

Abstract : This paper faces the problem of harvesting geographic information from Web documents, specifically, extracting facts on spatial relations among geographic places. The motivation is twofold. First, researchers on Spatial Data Mining often assume that spatial data are already available, thanks to current GIS and positioning technologies. Nevertheless, this is not applicable to the case of spatial information embedded in data without an explicit spatial modeling, such as documents. Second, despite the huge amount of Web documents conveying useful geographic information, there is not much work on how to harvest spatial data from these documents. The problem is particularly challenging because of the lack of annotated documents, which prevents the application of supervised learning techniques. In this paper, we propose to harvest facts on geographic places through an unsupervised approach which recognizes spatial relations among geographic places without supposing the availability of annotated documents. The proposed approach is based on the combined use of a spatial ontology and a prototype-based classifier. A case study on topological and directional relations is reported and commented.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00816292
Contributor : Mathieu Roche Connect in order to contact the contributor
Submitted on : Sunday, April 21, 2013 - 1:56:11 PM
Last modification on : Tuesday, September 7, 2021 - 3:55:02 PM

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Corrado Loglisci, Dino Ienco, Mathieu Roche, Maguelonne Teisseire, Donato Malerba. Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents. 12th International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM) @ ICDM, Dec 2012, Brussels, Belgium. pp.789-796, ⟨10.1109/ICDMW.2012.20⟩. ⟨lirmm-00816292⟩

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