Abstract
Analysis and processing of environmental information is considered of utmost importance for humanity. This article addresses the problem of discovery of web resources that provide environmental measurements. Towards the solution of this domain-specific search problem, we combine state-of-the-art search techniques together with advanced textual processing and supervised machine learning. Specifically, we generate domain-specific queries using empirical information and machine learning driven query expansion in order to enhance the initial queries with domain-specific terms. Multiple variations of these queries are submitted to a general-purpose web search engine in order to achieve a high recall performance and we employ a post processing module based on supervised machine learning to improve the precision of the final results. In this work, we focus on the discovery of weather forecast websites and we evaluate our technique by discovering weather nodes for south Finland.
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Moumtzidou, A., Vrochidis, S., Tonelli, S., Kompatsiaris, I., Pianta, E. (2012). Discovery of Environmental Nodes in the Web. In: Salampasis, M., Larsen, B. (eds) Multidisciplinary Information Retrieval. IRFC 2012. Lecture Notes in Computer Science, vol 7356. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31274-8_5
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DOI: https://doi.org/10.1007/978-3-642-31274-8_5
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