Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Semantic Data Integration in Hierarchical Domains
IEEE Intelligent Systems
Rondo: a programming platform for generic model management
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The Knowledge Engineering Review
The Piazza Peer Data Management System
IEEE Transactions on Knowledge and Data Engineering
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
Two types of hierarchies in geospatial ontologies
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
Optimal learning of ontology mappings from human interactions
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
Hi-index | 0.00 |
Semantic Interoperability is a major issue for National Spatial data Infrastructures (NSDIs) and mapping across heterogeneous databases is essential for such interoperability. Mapping of schemas based on ontology mapping provides opportunities for semantic translation of schemas elements and hence for database queries across heterogeneous sources. Such semantics based mappings are usually human centered processes. This paper demonstrates semi-automatic mapping using semantic similarity values from an electronic lexicon. Lexical similarity of class names and class structures constitute knowledge base for mapping between two schemas. We employ semantic mapping based on synonym similarity matches from WordNet. We use heuristics based propagation of similarities using attribute mapping and superclass-subclass relations. The machine based similarity values are seen to be comparable to human generated values of mapping.