Abstract
Entity resolution of geographical references is essential for the analysis of spatial temporal information when data come from heterogeneous sources. In the ConTraffic project we work on the analysis of trajectory of moving objects (e.g. commercial containers and vessels) and, as part of data processing step, the right location for textual references must be determined. In this paper we present an application of Bayesian networks that leverage the information of already resolved references in order to estimate the right entity corresponding to a geographical location. Contextual information of objects that have followed similar trajectories is used as well. Our approach is suitable to perform entity resolution efficiently even when the database contains millions of movements. The results we obtained prove that our method is useful in cases where string similarity methods are unable to provide a solution.
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Mazzola, L., Chahuara, P., Tsois, A., Pedone, M. (2014). Resolution of Geographical String Name through Spatio-Temporal Information. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_38
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DOI: https://doi.org/10.1007/978-3-319-08979-9_38
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