Extending the notion of data models or object models, 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. However, the generation of an effective ontology is a difficult task involving considerable labor and skill.
This paper discusses an Ontology Generation and Evolution Processor (OGEP) aimed at automating this process, only
requesting user input when un-resolvable ambiguous situations occur. OGEP directly attacks the main barrier which
prevents automated (or self learning) ontology generation: the ability to understand the meaning of artifacts and the
relationships the artifacts have to the domain space. OGEP leverages existing lexical to ontological mappings in the
form of WordNet, and Suggested Upper Merged Ontology (SUMO) integrated with a semantic pattern-based structure
referred to as the Semantic Grounding Mechanism (SGM) and implemented as a Corpus Reasoner. The OGEP
processing is initiated by a Corpus Parser performing a lexical analysis of the corpus, reading in a document (or corpus)
and preparing it for processing by annotating words and phrases. After the Corpus Parser is done, the Corpus Reasoner
uses the parts of speech output to determine the semantic meaning of a word or phrase. The Corpus Reasoner is the crux
of the OGEP system, analyzing, extrapolating, and evolving data from free text into cohesive semantic relationships.
The Semantic Grounding Mechanism provides a basis for identifying and mapping semantic relationships. By blending
together the WordNet lexicon and SUMO ontological layout, the SGM is given breadth and depth in its ability to
extrapolate semantic relationships between domain entities. The combination of all these components results in an
innovative approach to user assisted semantic-based ontology generation. This paper will describe the OGEP technology
in the context of the architectural components referenced above and identify a potential technology transition path to
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