Anchor-based large-scale ontologies partitioning and mapping
基于参考点的大规模本体分块与映射

In order to solve the problem of low precision and low recall of large-scale ontology partitioning and mapping, this paper proposed a new anchor-based large-scale ontology partitioning and mapping method. This method used anchors to guide partitioning, and partitioned the two ontologies at the same time, which called co-clustering. Firstly, it preprocessed the two ontologies in order to normalize the entities's name and turn them into tree structure, then used some simple methods to find anchors. At last, the anchors acted cluster centers to cluster the concepts in both ontology trees, and found block mappings at the same time. Theoretical analysis and experimental results show that this method both solves the large-scale ontologeis mapping problem and achieves good precision and recall.