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

Molina M, Sanchez-Soriano J, Corcho O - Sensors (Basel) (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Artificial Intelligence, Technical University of Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. martin.molina@upm.es.

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.

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Main causes of incorrect descriptions ((a) average and (b) best geographic area).
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sensors-15-16009-f007: Main causes of incorrect descriptions ((a) average and (b) best geographic area).

Mentions: We analyzed the main causes of the generation of incorrect descriptions. As a result, we can distinguish the following situations (Figure 7):


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

Molina M, Sanchez-Soriano J, Corcho O - Sensors (Basel) (2015)

Main causes of incorrect descriptions ((a) average and (b) best geographic area).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541865&req=5

sensors-15-16009-f007: Main causes of incorrect descriptions ((a) average and (b) best geographic area).
Mentions: We analyzed the main causes of the generation of incorrect descriptions. As a result, we can distinguish the following situations (Figure 7):

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Artificial Intelligence, Technical University of Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. martin.molina@upm.es.

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.

No MeSH data available.


Related in: MedlinePlus