IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Facilitating the Exchange of Explicit Knowledge through Ontology Mappings
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Ontology-driven geographic information systems
Ontology-driven geographic information systems
Uncertainty in the Automation of Ontology Matching
ISUMA '03 Proceedings of the 4th International Symposium on Uncertainty Modelling and Analysis
A new model of evaluating concept similarity
Knowledge-Based Systems
Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis
Knowledge-Based Systems
A new case-based classification using incremental concept lattice knowledge
Data & Knowledge Engineering
A social dimensional cyber threat model with formal concept analysis and fact-proposition inference
International Journal of Information and Computer Security
| Hi-index | 0.00 |
With the rapid development of the semantic web, it is likely that the number of ontologies will greatly increase during the next few years, which leads to the arising demand for rapid ontology mapping. In this paper, a novel similarity measure method based on rough set and concept lattice is proposed to realize ontology mapping tasks. A reference concept lattice is first constructed with the combination of two normalized contexts. Rough set theory is then employed to calculate the similarity measure of the two ontology nodes. With a specified threshold, the final result of ontology mapping can be obtained. Compared with other mapping algorithms, the proposed ontology mapping method is featural and structural, and the experiment shows the performance of the mapping method.