Computers and Biomedical Research
The design and implementation of CoBase
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CoBase: a cooperative database system
Nonstandard queries and nonstandard answers
CoBase: a scalable and extensible cooperative information system
Journal of Intelligent Information Systems - Special issue on intelligent integration of information
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Global Viewing of Heterogeneous Data Sources
IEEE Transactions on Knowledge and Data Engineering
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
Promptdiff: a fixed-point algorithm for comparing ontology versions
Eighteenth national conference on Artificial intelligence
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
Journal of Biomedical Informatics
An ontological modeling approach to cerebrovascular disease studies: The NEUROWEB case
Journal of Biomedical Informatics
Comparing drug-class membership in ATC and NDF-RT
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Extended ontological model for distance learning purpose
PAKM'06 Proceedings of the 6th international conference on Practical Aspects of Knowledge Management
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The retrieval and exchange of information between medical databases is often impeded by the semantic heterogeneity of concepts contained within the databases. Manual identification of equivalent database elements consumes time and resources, and may often be the rate-limiting technological step in integrating disparate data sources. By employing semantic networks as an intermediary representation of the native databases, automated mapping algorithms can identify equivalent concepts in disparate databases. The algorithms take advantage of the conceptual "context" embodied within a semantic network to produce candidate concept mappings. The performance of automated concept mapping was evaluated by creating semantic network representations for two test laboratory databases. The mapping algorithms identified all equivalent concepts that were present in the databases, and did not leave any equivalent concepts unmapped. The utilization of conceptual context to perform automated concept mapping facilitates the identification of equivalent database concepts and may help decrease the work and costs associated with retrieval and integration of information from disparate databases.