Matching Large Ontologies Based on Reduction Anchors
Peng Wang, Yuming Zhou, Baowen Xu
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.