Text Analysis with Ontology Reasoning
The analysis of unstructured text when performed by text mining machine learning algorithm results in mining model holding rules for relationships and dependencies among terms extracted by text preprocessing techniques. The obtained mining model represents knowledge derived from the analyzed text which is hard to interpret as it lacks context. Enhancement of its semantic value can be obtained by implementing logic based approach providing formally defined meaning and interpretation mechanism. The generally accepted form for representation of knowledge for existing domain is by domain specific ontology. The aim of the paper consists in designing a framework for performing ontology instantiation and population with the structures of a complex mining model involving classification and association rules. Procedures have been designed for annotating them with domain concepts and semantic types. The framework provides for turning the mining model into a context model. Ontology reasoning is implemented to validate the input mining model by rule semantic disambiguation and dependency conceptualization. The framework implementation provides for outputting validated domain related knowledge base in explicit and machine readable form as a resource that can be adapted for decision support.