Overview of YAM - (Not) Yet Another Matcher for Ontology Alignment Task
23 Pages Posted: 11 Jul 2018 Publication Status: Accepted
Several challenges to the field of ontology matching have been outlined in recent research. The selection of the appropriate similarity measures as well as the configuration tuning of their combination are known as fundamental issues the community should deal with. Verifying the semantic coherence of the discovered alignment is also known as a crucial task. As the challenging issues are both in basic matching techniques and in their combination, our approach is aimed to provide improvement at the basic matcher level and also at the level of framework. Matching large scale ontologies is currently one of the most challenging issues in ontology matching field. The main reason is that large ontologies are highly heterogeneous both at terminological and conceptual levels. Furthermore, matching very large ontologies entails exploring a very large searching space to discover correspondences. It may also require a huge amount of main memory to maintain the temporary results at each computational step. These factors strongly impact the effectiveness and efficiency of any ontology matching tool. To overcome these issues, we have developed a disk-based ontology matching approach. The underlying idea of our approach is that the complexity and therefore the cost of the matching algorithms are reduced thanks to the indexing data structures by avoiding exhaustive pair-wise comparisons. Indeed, we extensively used indexing techniques in many places. For example, we defined a bitmap encoding the structural information of an ontology. This indexing structure will be exploited for accelerating similarity propagation. Moreover, our approach uses a disk-based mechanism to store temporary data. This allows to perform any ontology matching task on a simple PC or laptop instead of a powerful server.In this paper, we describe YAM , an ontology matching tool, aimed at solving these issues. We evaluated the efficiency of YAM in various OAEI 2012 and OAEI 2013 tracks. YAM was one of the best ontology matching systems in terms of F-measure. Most notably, the current version of YAM has passed all scalability and large scale ontology matching tests and obtained high matching quality results.
Keywords: Ontology Matching, Similarity Measure, Matcher Combination, Similarity Propagation, Mapping Selection, Large Scale Ontology Matching
Suggested Citation: Suggested Citation