Affiliations: [a] Heinz-Nixdorf Chair for Distributed Information Systems, Institute for Computer Science, Friedrich Schiller University Jena, Germany | [b] Michael-Stifel-Center for Data-Driven and Simulation Science, Jena, Germany
Abstract: Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present CoMerger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.
