Data-Driven Semantic Concept Analysis For Automatic Actionable Ontology Design

The paper objectives are twofold: to discuss the essence and challenges of automatic ontology design as applied to the Big data semantic modeling and to present Semantic Concept Analysis (SCA), a framework specifically developed for automatic actionable ontology design in Big data scenario. This framework integrates the data-driven DBpedia-based technology for semi-automatic design of the ontology concept hierarchy and Formal Concept Analysis (FCA), which formal concept specialization structure is built as dual one with regard to the ontology concept hierarchy. The SCA model of big data is built iteratively through interleaving use of data-driven ontology generalization step and subsequent formal concept specialization step. In this procedure, each of the pair of steps controls the other one. Indeed, ontology generalization step determines the dual formal concepts of the next specialization level, whereas the extent cardinality of each generated formal concept is used as attribute of the stopping criterion for the iterative ontology generalization design process. The proposed SCA framework technology is validated experimentally through its software prototyping and subsequent computer experimentation.