Original Contribution: Stacked generalization
Neural Networks
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
| Hi-index | 0.00 |
As a crucial point of semantic integration, ontology mapping has been researched in-depth in recent years. In this paper, the problem of concept similarity computing is regarded as the problem of classifying, and a multi-strategy ontology mapping framework based on Stacking method is proposed with the multi concept similarity computing strategies being combined with Stacking method; also, a novel metadata classifying algorithm LMSMC based on Widrow-Hoff theory is presented. In this framework, the target ontology is classified with various current concept similarity computing methods in layer 0, and the metadata is classified with LMSMC algorithm in layer 1, and then the ontology mapping which combines several algorithms is made. Experiments indicate that the method perfonns better in improving precision and recall compared to individually using those algorithms.