Structural knowledge extraction from mobility data

Risultato della ricerca: Other

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.
Lingua originaleEnglish
Pagine294-307
Numero di pagine14
Stato di pubblicazionePublished - 2016

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Knowledge Extraction
Grammar
Grammatical Inference
Search Problems
Structural Model
Availability
Formulation
Operator
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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title = "Structural knowledge extraction from mobility data",
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.",
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AU - Lo Re, Giuseppe

AU - Ortolani, Marco

AU - Cottone, Pietro

AU - Gaglio, Salvatore

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AB - 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.

UR - http://hdl.handle.net/10447/219842

UR - https://link.springer.com/chapter/10.1007%2F978-3-319-49130-1_22

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