Automatic ontology merging by hierarchical clustering and inference mechanisms

One of the core challenges for current landscape of ontology based research is to develop efficient ontology merging algorithms which can resolve the mismatches with no or minimum human intervention, and generate automatic global merged ontology on-the-fly to fulfil the needs of automated enterprise business applications and mediation based data warehousing. This paper presents our approach of ontology merging in context of data warehousing by mediation that aims at building analysis contexts on-the-fly. Our methodology is based on the combination of the statistical aspect represented by the hierarchical clustering technique and the inference mechanism. It generates the global ontology automatically by four steps. First, it builds classes of equivalent entities of different categories (concepts, properties, instances) by applying a hierarchical clustering algorithm. Secondly, it makes inference on detected classes to find new axioms, and solves synonymy and homonymy conflicts. This step also consists of generating sets of concept pairs from ontology hierarchies, such as the first component subsumes the second one. Third, it merges different sets together, and uses classes of synonyms and sets of concept pairs to solve semantic conflicts in the global set of concept pairs. Finally, it transforms this set to a new hierarchy, which represents the global ontology. -- Ontology merging ; similarity measure ; hierarchical clustering and inference ; data warehouse design ; data mining