基于参考点的大规模本体扩散映射算法

Anchor Based Flood Mapping Algorithm for Large-scale Ontology

目前针对大规模本体映射大多采用先分块,再在分得的小块间进行块内映射.但是,对大本体进行分块,以及对相关小块的映射操作会增加很大的开销.本文采用基于参考点的扩散映射算法,通过小模块快速获得参考点(在名称上相似的概念),利用访问局部性原理,通过比较参考点附近的邻居概念,逐渐向其邻居扩散映射.根据邻居概念的映射情况检查参考点是否为错误映射,且映射的邻居概念成为新的参考点.然后对新的参考点迭代进行扩散映射,直到所有概念都扩散完毕或者找不到新的参考点为止.显然,该方法把候选映射概念集始终限制在参考点附近,极大的减少了映射的时间复杂度.本文算法支持从目标本体到源本体的1:n映射;支持参考点的自动生成;带冲突避免的映射操作能同时提高映射效率和质量.

The existing large-scale ontologies strategies are mostly based on partition and then mapping in the small segmented blocks.How ever,the process of partitioning and finding related segmented blocks is time-consuming.Therefore,a anchor based flood mapping algorithm is proposed,w hich starts off w ith an anchor,a pair of ＂ look-alike＂ concepts from each ontology,gradually checking old anchors and exploring new anchors by comparing concepts around the anchors taking advantage of locality of reference in the RDF directed graph.The process is repeated after new anchor is gained until there is no new anchor found.Obviously,the candidate sets for comparison are restricted around the anchors all along,hence,time complexity is greatly reduced by minimizing the comparisons betw een entities.Most importantly,in our algorithm,most 1： n mappings from the target ontology to the source ontology can be discovered;anchor auto-search module is designed;flood operation w ith collision avoidance can improve both mapping efficiency and quality.