The main aim of this project was to design and implement a system able to infer the weather state of a location for a specific date by applying Bayesian inference models and statistical analysis on web observations. Additionally, we investigated various linear combinations of probabilistic schemes where traffic information, previous day's weather or a weather prior probability contribute to the final decision. As a final extension, we visualised the weather inference results on a map. Software packages and a weather ontology were developed for data collection and preprocessing. Parametrised Bayesian belief networks formed the expression of probabilistic correlation between the inferred and the official weather observations. During training, we decide the optimal parameters and then test their absolute and relative performance. Experimental results indicate that the absolute and relative (p-values) performance in most of the schemes is significant. As a result, one may assume that similar or even more sophisticated information extraction models on different contexts will be able to deliver useful conclusions. Despite the amount of work carried out, and the significant results we retrieved, there is still space for improvements. Recommendations for further work are given lastly.
|Translated title of the contribution||Weather Talk - extracting weather information by text mining|
|Number of pages||67|
|Publication status||Published - 30 Sep 2008|