Matching Large Ontologies Based on Reduction Anchors
Peng Wang
More and more large ontologies play important roles in the fields such as digital library, information retrieval, and bioinformatics. For the reason that large ontologies themselves still can be heterogeneous, it is necessary to matching various large ontologies to enable cooperation between them.However, due to the high time and space complexity, most existing ontology matching systems are not well scalable to solve this problem. The feasible solutions proposed in recent years are based on the divide-and-conquer strategy, which partitions large ontologies into small ones that the previous matching methods can tackle with.However, besides partitioning ontology is a complicate process, it will lead to some semantic information loss during the matching.To avoid these drawbacks, this paper proposes a new large ontology matching method based on reduction anchors. Our method has two distinct advantages: First, it needs not to partition the large ontologies; Second, it is a general method that can utilize most ontology matching techniques. The contributions of this paper include two parts: (1) Two kinds of reduction anchors, positive and negative reduction anchors, are proposed to reduce the time complexity problem in matching. Positive reduction anchors use the ontology hierarchy feature to predict the ignorable similarity calculations. Negative reduction anchors use the locality of matching to predict the ignorable similarity calculations. (2) The experiments show that the reduction-anchor-based approach is effective for matching large ontologies.