Advanced Self-Learning Ontologies

Ontology development has increased over the last few years concurrent with the rise in popularity of web services and other technologies positioned to leverage the semantic capabilities of an ontology-based solution. Extending the notion of data models or object models, an ontology can provide rich semantic definition not only to the meta-data but also to the instance data of domain knowledge, making these semantic definitions available in machine readable form. While the value and potential of a well structured ontology is rarely debatable, the promise of ontology is infrequently realized due to the labor extensive process and specialized skill set required to develop an effective ontology. The ability of ontologies to learn, or evolve to changing information structure is critical to maximizing their benefits to solve key knowledge management challenges within a net-centric environment. The Securboration Team has developed an innovative approach to break the main barrier which prevents self-learning of ontologies: the ability to understand the meaning of artifacts and relationships the artifacts have to the domain space. Our approach, called Ontology Generation and Evolution Processor (OGEP), leverages existing lexical to ontological mappings integrated in an innovative approach to semantic-based ontology generation.