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Sensors 2015, 15(7), 16009-16026; doi:10.3390/s150716009

Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks

Department of Artificial Intelligence, Technical University of Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 29 April 2015 / Revised: 12 June 2015 / Accepted: 25 June 2015 / Published: 3 July 2015
(This article belongs to the Section Sensor Networks)
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Abstract

Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions. View Full-Text
Keywords: sensor network; natural language generation; open geographic data sensor network; natural language generation; open geographic data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Molina, M.; Sanchez-Soriano, J.; Corcho, O. Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks. Sensors 2015, 15, 16009-16026.

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