Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Comparing two approaches for aligning representations of anatomy
Artificial Intelligence in Medicine
Just the right amount: extracting modules from ontologies
Proceedings of the 16th international conference on World Wide Web
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Constructing virtual documents for ontology matching using mapreduce
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
Ontology matching benchmarks: Generation, stability, and discriminability
Web Semantics: Science, Services and Agents on the World Wide Web
OntoPart: at the cross-roads of ontology partitioning and scalable ontology alignment systems
International Journal of Metadata, Semantics and Ontologies
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Matching large ontologies is a challenge due to the high time complexity. This paper proposes a new matching method for large ontologies based on reduction anchors. This method has a distinct advantage over the divide-and-conquer methods because it dose not need to partition large ontologies. In particular, two kinds of reduction anchors, positive and negative reduction anchors, are proposed to reduce the time complexity in matching. Positive reduction anchors use the concept hierarchy to predict the ignorable similarity calculations. Negative reduction anchors use the locality of matching to predict the ignorable similarity calculations. Our experimental results on the real world data sets show that the proposed method is efficient for matching large ontologies.