Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
ACM SIGMOD Record
ACM SIGMOD Record
Matching large schemas: Approaches and evaluation
Information Systems
Semi-automatic schema integration in Clio
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
Rewrite techniques for performance optimization of schema matching processes
Proceedings of the 13th International Conference on Extending Database Technology
Element similarity measures in XML schema matching
Information Sciences: an International Journal
Matching large scale ontology effectively
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Managing uncertainty in schema matching with top-k schema mappings
Journal on Data Semantics VI
An ontology-driven framework towards building enterprise semantic information layer
Advanced Engineering Informatics
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Schema and ontology matching have attracted a great deal of interest among researchers. Despite the advances achieved, the large matching problem still presents a real challenge, such as it is a time-consuming and memory-intensive process. We therefore propose a scalable, clustering-based matching approach that breaks up the large matching problem into smaller matching problems. In particular, we first introduce a structure-based clustering approach to partition each schema graph into a set of disjoint subgraphs (clusters). Then, we propose a new measure that efficiently determines similar clusters between every two sets of clusters to obtain a set of small matching tasks. Finally, we adopt the matching prototype COMA++ to solve individual matching tasks and combine their results. The experimental analysis reveals that the proposed method permits encouraging and significant improvements.