Information retrieval and machine learning for probabilistic schema matching (Articolo in rivista)

Type
Label
  • Information retrieval and machine learning for probabilistic schema matching (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.ipm.2006.10.014 (literal)
Alternative label
  • Nottelmann H.; Straccia U. (2007)
    Information retrieval and machine learning for probabilistic schema matching
    in Information processing & management
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Nottelmann H.; Straccia U. (literal)
Pagina inizio
  • 552 (literal)
Pagina fine
  • 576 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.sciencedirect.com/science/article/pii/S0306457306001907 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 43 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Information Processing & Management, vol. 43 (30) pp. 552 - 576. Elsevier Science, 2007. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Informatics, University of Duisburg-Essen, 47048 Duisburg, Germany, ISTI-CNR, Via G. Moruzzi 1, 56124 Pisa, Italy (literal)
Titolo
  • Information retrieval and machine learning for probabilistic schema matching (literal)
Abstract
  • Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distributed IR in federated digital libraries. This paper introduces a probabilistic framework, called sPLMap, for automatically learning schema mapping rules, based on given instances of both schemas. Different techniques, mostly from the IR and machine learning fields, are combined for finding suitable mapping candidates. Our approach gives a probabilistic interpretation of the prediction weights of the candidates, selects the rule set with highest matching probability, and outputs probabilistic rules which are capable to deal with the intrinsic uncertainty of the mapping process. Our approach with different variants has been evaluated on several test sets. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Autore CNR di
Prodotto
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Insieme di parole chiave di
data.CNR.it