Thayasivam Uthayasanker, Doshi Prashant
THINC Lab, Dept. of Computer Science, University of Georgia, Athens, GA 30602, USA,
THINC Lab, Dept. of Computer Science University of Georgia, Athens, GA 30602, USA,
Proc IEEE Int Conf Semant Comput. 2013 Sep;2013:110-113. doi: 10.1109/ICSC.2013.28.
Real-world ontologies tend to be very large with several containing thousands of entities. Increasingly, ontologies are hosted in repositories, which often compute the alignment between the ontologies. As new ontologies are submitted or ontologies are updated, their alignment with others must be quickly computed. Therefore, aligning several pairs of ontologies quickly becomes a challenge for these repositories. We project this problem as one of batch alignment and show how it may be approached using the distributed computing paradigm of MapReduce. Our approach allows any alignment algorithm to be utilized on a MapReduce architecture. Experiments using four representative alignment algorithms demonstrate flexible and significant speedup of batch alignment of large ontology pairs using MapReduce.
现实世界的本体往往非常庞大，有些包含数千个实体。本体越来越多地存放在存储库中，这些存储库通常会计算本体之间的对齐关系。随着新本体的提交或本体的更新，必须快速计算它们与其他本体的对齐关系。因此，快速对齐多对本体对这些存储库来说很快就成为了一项挑战。我们将这个问题作为批量对齐问题之一，并展示如何使用MapReduce的分布式计算范式来解决它。我们的方法允许在MapReduce架构上使用任何对齐算法。使用四种代表性对齐算法进行的实验表明，使用MapReduce可以灵活且显著地加速大型本体对的批量对齐。
