A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Discovering complex matchings across web query interfaces: a correlation mining approach
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Industrial-strength schema matching
ACM SIGMOD Record
Holistic Query Interface Matching using Parallel Schema Matching
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Enterprise information mashups: integrating information, simply
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Matching large schemas: Approaches and evaluation
Information Systems
A survey of schema-based matching approaches
Journal on Data Semantics IV
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Computational complexity of schema matching approaches
MAMECTIS'09 Proceedings of the 11th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Optimization and comparison of schema matching solutions
MAMECTIS'09 Proceedings of the 11th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Computational requirement of schema matching algorithms
WSEAS Transactions on Information Science and Applications
Calibration and comparison of schema matchers
WSEAS Transactions on Mathematics
Tuning the ensemble selection process of schema matchers
Information Systems
Techniques for discovering correspondences between ontologies
International Journal of Web and Grid Services
| Hi-index | 0.00 |
Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust mapping method which creates a mediated schema tree from a large set of input XML schema trees and defines mappings from the contributing schema to the mediated schema. The result is an almost automatic technique giving good performance with approximate semantic match quality. Our method uses node ranks calculated by pre-order traversal. It combines tree mining with semantic label clustering which minimizes the target search space and improves performance, thus making the algorithm suitable for large scale data sharing. We report on experiments with up to 80 schemas containing 83,770 nodes, with our prototype implementation taking 587 seconds to match and merge them to create a mediated schema and to return mappings from input schemas to the mediated schema.