PSOM2—partitioning-based scalable ontology matching using MapReduce

Abstract

The growth and use of semantic web has led to a drastic increase in the size, heterogeneity and number of ontologies that are available on the web. Correspondingly, scalable ontology matching algorithms that will eliminate the heterogeneity among large ontologies have become a necessity. Ontology matching algorithms generally do not scale well due to the massive number of complex computations required to achieve matching. One of the methods used to address this problem is the use of partition-based systems to reduce the matching space. In this paper, we propose a new partitioning-based scalable ontology matching system called PSOM2. We have designed a new neighbour-based intra-similarity measure to increase the quality of the cluster set formation for the partition-based ontology matching process. These sets of clusters or sub-ontologies are matched across the input ontologies to identify matchable cluster pairs, based on anchors that are efficiently discovered through a new light-weight linguistic matcher (EI-sub). However, in order to further increase the efficiency of the time-consuming anchor discovery process we have designed a MapReduce-based EI-sub process where anchors are discovered in distributed and parallel fashion. Experiments on benchmark OAEI (Ontology Alignment Evaluation Initiative) large scale ontologies demonstrate that the new PSOM2 system achieves, on an average, 31% decrease in entropy of the clusters and 54.5% reduction in overall run time. Based on the experimental results, it is evident that the new PSOM2 achieves better quality clusters and a major reduction in execution time, leading to an effective and scalable ontology matching system.