Combining logic and probabilities for discovering mappings between taxonomies

Authors:
Rémi Tournaire;Jean-Marc Petit;Marie-Christine Rousset;Alexandre Termier
Affiliations:
University of Grenoble, Laboratory of Informatics of Grenoble, UMR, St-Martin d'Hères Cedex, France;INSA Lyon, LIRIS, UMR, Villeurbanne Cedex, France;University of Grenoble, Laboratory of Informatics of Grenoble, UMR, St-Martin d'Hères Cedex, France;University of Grenoble, Laboratory of Informatics of Grenoble, UMR, St-Martin d'Hères Cedex, France
Venue:
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Year:
2010

Citing 18
Cited 0

SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks

Data & Knowledge Engineering
Learning to map between ontologies on the semantic web

Proceedings of the 11th international conference on World Wide Web
Introduction to Algorithms

Introduction to Algorithms
Efficient SVM Regression Training with SMO

Machine Learning
Generic Schema Matching with Cupid

Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching

The VLDB Journal — The International Journal on Very Large Data Bases
Corpus-Based Schema Matching

ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Schema and ontology matching with COMA++

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Answering queries from statistics and probabilistic views

VLDB '05 Proceedings of the 31st international conference on Very large data bases
ULDBs: databases with uncertainty and lineage

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Ontology Matching

Ontology Matching
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
COMA: a system for flexible combination of schema matching approaches

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Semi-automatic schema integration in Clio

VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Data integration with uncertainty

The VLDB Journal — The International Journal on Very Large Data Bases
Integrating multiple internet directories by instance-based learning

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
FCA-MERGE: bottom-up merging of ontologies

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Mapping validation by probabilistic reasoning

ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach.