Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Piazza: data management infrastructure for semantic web applications
WWW '03 Proceedings of the 12th international conference on World Wide Web
Applying Data Warehouse Concepts to Gene Expression Data Management
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Ontology mapping: the state of the art
The Knowledge Engineering Review
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Introduction to the special issue on semantic integration
ACM SIGMOD Record
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
HePToX: marrying XML and heterogeneity in your P2P databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
AI Magazine - Special issue on semantic integration
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Discovering the semantics of relational tables through mappings
Journal on Data Semantics VII
Hi-index | 0.00 |
There is an increasing demand for discovering meaningfulrelationships, i.e., mappings, between conceptual models for interoperability. Current solutions have been focusing on the discovery of correspondences between elements in different conceptual models. However, a complexmapping associating a structure connecting a set of elements in one conceptual model with a structure connecting a set of elements in another conceptual model is required in many cases. In this paper, we propose a novel technique for discovering semantically similar associations (SeSA) for constructing complex mappings. Given a pair of conceptual models, we create a mapping graphby taking the cross product of the two conceptual model graphs. Each edge in the mapping graph is assigned a weight based on the semantic similarity of the two elements encoded by the edge. We then turn the problem of discovering semantically similar associations (SeSA) into the problem of finding shortest paths in the mapping graph. We experiment different combinations of values for element similarities according to the semantic types of the elements. By choosing the set of values that have the best performance on controlled mapping cases, we apply the algorithm on test conceptual models drawn from a variety of applications. The experimental results show that the proposed technique is effective in discovering semantically similar associations (SeSA).