Speeding up Batch Alignment of Large Ontologies Using MapReduce.

Abstract

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.