WordNet: a lexical database for English
Communications of the ACM
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
GAOM: Genetic Algorithm Based Ontology Matching
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
Using Partial Reference Alignments to Align Ontologies
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Towards imaging large-scale ontologies for quick understanding and analysis
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Discrete particle swarm optimisation for ontology alignment
Information Sciences: an International Journal
A hybrid evolutionary approach for solving the ontology alignment problem
International Journal of Intelligent Systems
| Hi-index | 12.05 |
All the state of the art approaches based on evolutionary algorithm (EA) for addressing the meta-matching problem in ontology alignment require the domain expert to provide a reference alignment (RA) between two ontologies in advance. Since the RA is very expensive to obtain especially when the scale of ontology is very large, in this paper, we propose to use the Partial Reference Alignment (PRA) built by clustering-based approach to take the place of RA in the process of using evolutionary approach. Then a problem-specific Memetic Algorithm (MA) is proposed to address the meta-matching problem by optimizing the aggregation of three different basic similarity measures (Syntactic Measure, Linguistic Measure and Taxonomy based Measure) into a single similarity metric. The experimental results have shown that using PRA constructed by our approach in most cases leads to higher quality of solution than using PRA built in randomly selecting classes from ontology and the quality of solution is very close to the approach using RA where the precision value of solution is generally high. Comparing to the state of the art ontology matching systems, our approach is able to obtain more accurate results. Moreover, our approach's performance is better than GOAL approach based on Genetic Algorithm (GA) and RA with the average improvement up to 50.61%. Therefore, the proposed approach is both effective.