WordNet: a lexical database for English
Communications of the ACM
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Web ontology segmentation: analysis, classification and use
Proceedings of the 15th international conference on World Wide Web
Ontology Matching
Comparing two approaches for aligning representations of anatomy
Artificial Intelligence in Medicine
Falcon-AO: A practical ontology matching system
Web Semantics: Science, Services and Agents on the World Wide Web
A Flexible Partitioning Tool for Large Ontologies
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
When owl: sameAs isn't the same: an analysis of identity in linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
The hidden web, XML and the Semantic Web: scientific data management perspectives
Proceedings of the 14th International Conference on Extending Database Technology
Designing the web for an open society
Proceedings of the 20th international conference on World wide web
A survey of schema-based matching approaches
Journal on Data Semantics IV
Matching large ontologies based on reduction anchors
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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In the semantic web register, ontologies are the cornerstones for many applications thanks to their expressiveness ability to explicate shared conceptualisations. Neverthless, the steady growth of these ontologies made their management beyond human capabilities. Thus, ontology partitioning seems to be a thriving issue for tackling such oversized ontologies and their administration through several systems. In this respect, ontology alignment task is an important component of the integration systems by providing a solution for data heterogeneousness by allowing their interoperability. In addition, such used alignment techniques implement complex computations that are losing their effectiveness against the scalability problem. To meet this challenge, we introduce in this paper a partitioning method, designed to take into account the goal of the alignment task. This method allows to split both ontologies to be aligned in two reduced size block sets, containing the elements capable to be matched. The results of tests performed with our method on different pairs of ontologies show their effectiveness.