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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.

No MeSH data available.


Related in: MedlinePlus

Number of sensors in the evaluation dataset for each geographic area; (a) number of sensors; (b) geographic areas.
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sensors-15-16009-f005: Number of sensors in the evaluation dataset for each geographic area; (a) number of sensors; (b) geographic areas.

Mentions: The dataset includes a total of 1811 sensors distributed in 10 geographic areas (Figure 5). In order to build the evaluation dataset it was necessary to clean and filter the original data in order to select the appropriate information useful for our problem. For example, we removed non-useful attributes (e.g., specific identifiers or flags related to their operational status).


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

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

Number of sensors in the evaluation dataset for each geographic area; (a) number of sensors; (b) geographic areas.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16009-f005: Number of sensors in the evaluation dataset for each geographic area; (a) number of sensors; (b) geographic areas.
Mentions: The dataset includes a total of 1811 sensors distributed in 10 geographic areas (Figure 5). In order to build the evaluation dataset it was necessary to clean and filter the original data in order to select the appropriate information useful for our problem. For example, we removed non-useful attributes (e.g., specific identifiers or flags related to their operational status).

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