Pietro Cottone
Structural Knowledge Extraction from Mobility Data
Cottone, Pietro; Gaglio, Salvatore; Lo Re, Giuseppe; Ortolani, Marco; Pergola, Gabriele
Authors
Contributors
Giovanni Adorni
Editor
Stefano Cagnoni
Editor
Marco Gori
Editor
Marco Maratea
Editor
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, November). Structural Knowledge Extraction from Mobility Data. Presented at XVth International Conference of the Italian Association for Artificial Intelligence, Genova, Italy
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | XVth International Conference of the Italian Association for Artificial Intelligence |
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 |
Public URL | https://keele-repository.worktribe.com/output/668214 |
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 |
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