6533b824fe1ef96bd128018b
RESEARCH PRODUCT
Structural Knowledge Extraction from Mobility Data
Salvatore GaglioPietro CottoneMarco OrtolaniGabriele PergolaGiuseppe Lo Resubject
Process (engineering)Computer scienceGeneralizationmedia_common.quotation_subjectInference02 engineering and technologyMachine learningcomputer.software_genreTheoretical Computer ScienceGrammatical inferenceKnowledge extractionRule-based machine translation020204 information systems0202 electrical engineering electronic engineering information engineeringSearch problemmedia_commonStructural knowledgeGrammarbusiness.industryMobility dataComputer Science (all)020207 software engineeringGrammar inductionArtificial intelligencebusinesscomputerNatural language processingdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |