Taxonomy alignment for interoperability between heterogeneous virtual organizations

Resources in virtual organizations are classified based on their local taxonomies. However, heterogeneity between these taxonomies is a serious problem for efficient cooperation processes (e.g., knowledge sharing and querying-based interactions). In order to overcome this problem, we propose a novel framework based on aligning the taxonomies of virtual organizations. Thereby, the best mapping between two organization taxonomies has to be discovered to maximize the summation of a set of partial similarities between concepts in the taxonomies. We can consider two levels of alignment processes; (i) intra-alignment in a virtual organization for building an organizational taxonomy and (ii) inter-alignment between organizational taxonomies. Particularly, for intra-alignment, features extracted from resources are exploited to enhance the precision of similarity measurement between concepts. For experimentation, twelve virtual organizations have been built with different local taxonomies. The proposed inter-alignment method has shown about 76% of precision and 68% of recall. Also, feature-based intra-alignment improved those performance, during resource retrieval by query transformation. In addition, we found out that alignment results are dependent on some characteristics of taxonomies (e.g., depth and number of classes).