Information retrieval and machine learning for probabilistic schema matching

Authors:
Henrik Nottelmann;Umberto Straccia
Affiliations:
University of Duisburg-Essen, Duisburg, Germany;ISTI-CNR, Pisa, Italy
Venue:
Proceedings of the 14th ACM international conference on Information and knowledge management
Year:
2005

Citing 4
Cited 1

Reconciling schemas of disparate data sources: a machine-learning approach

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data integration: a theoretical perspective

Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Exchange: Semantics and Query Answering

ICDT '03 Proceedings of the 9th International Conference on Database Theory
sPLMap: a probabilistic approach to schema matching

ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research

Optimal learning of ontology mappings from human interactions

OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas. This paper presents a probabilistic framework, called sPLMap, for automatically learning schema mapping rules. Similar to LSD, different techniques, mostly from the IR field, are combined.Our approach, however, is also able to give a probabilistic interpretation of the prediction weights of the candidates, and to select the rule set with highest matching probability.