Matching Network of Ontologies: a Pattern Recognition Approach

Networks of Ontologies research deals with the need to combine several ontologies at the same time. In a world of integrated systems (system of systems), isolated systems are increasingly rare in the near future, and their integration creates opportunities to change, validate information and add more value to an information system. This system of systems can contain ontologies to support the corresponding knowledge model. Consequently, new integration requirements may have to deal with network alignment rather than single ontologies. This work delves into the area of network alignment and proposes new ways to approach a particular case of alignment of large ontologies. The contribution of the work is the use of algebraic operations on networks to eliminate candidates before alignment and to use a stochastic search method to discover the relevant nodes. These nodes should be retained as they increase accuracy and final alignment retrieval even though they are identical and removed by the algebraic operation. To find out the particular relevance of each node, we propose a random walk combined with a frequent itemsets approach that overcomes the force brute approaches in processing time, as the size of networks grows, and have close precision. The approach was validated using networks of ontologies created from the OAEI ontologies. The approach selected the entities to send to the matcher without losing significant preexisting alignments. Finally, two different matchers were used to get metrics and compare the results with the pairwise force brute approach.