Structural Knowledge Extraction from Mobility Data

Cottone, Pietro; Gaglio, Salvatore; Lo Re, Giuseppe; Ortolani, Marco; Pergola, Gabriele

Abstract

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will finally present an application to the extraction of structural models representing user mobility behaviors, based on public datasets.

Citation

Cottone, P., Gaglio, S., Lo Re, G., Ortolani, M., & Pergola, G. (2016). Structural Knowledge Extraction from Mobility Data. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances in Artificial Intelligence -. https://doi.org/10.1007/978-3-319-49130-1_22

• Conference Name: XVth International Conference of the Italian Association for Artificial Intelligence
• Conference Location: Genova, Italy
• Start Date: Nov 29, 2016
• End Date: Dec 1, 2016
• Publication Date: 2016
• Deposit Date: Dec 14, 2023
• Publisher: Springer
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743; 1611-3349
• Book Title: AI*IA 2016 Advances in Artificial Intelligence -
• ISBN: 9783319491295; 9783319491301
• DOI: https://doi.org/10.1007/978-3-319-49130-1_22
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-319-49130-1_22
• Related Public URLs: https://link.springer.com/book/10.1007/978-3-319-49130-1