An efficient schema matching algorithm

Authors:
Wei Cheng;Heshui Lin;Yufang Sun
Affiliations:
Institute of Software, Chinese Academy of Sciences, Beijing, China;Institute of Software, Chinese Academy of Sciences, Beijing, China;Institute of Software, Chinese Academy of Sciences, Beijing, China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

Citing 9
Cited 0

WordNet: a lexical database for English

Communications of the ACM
SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks

Data & Knowledge Engineering
A vision for management of complex models

ACM SIGMOD Record
Conceptual linking: ontology-based open hypermedia

Proceedings of the 10th international conference on World Wide Web
Reconciling schemas of disparate data sources: a machine-learning approach

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
COMA: a system for flexible combination of schema matching approaches

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

Schema matching, the problem of finding semantic correspondences between elements of source and warehouse schemas, plays a key role in data warehousing. Currently, the mappings are largely determined manually by domain experts, thus a time-consuming process. In this paper, based on a multistrategy schema matching framework, we develop a linguistic matching algorithm using semantic distances between words to compute their semantic similarity, and propose a structural matching algorithm based on semantic similarity propagation. After describe our approach, we present experimental results on several real-world domains, and show that the algorithm discovers semantic mappings with a high degree of accuracy.