Automatic Knowledge Acquisition and Integration Technique: Application to Large Scale Taxonomy Extraction and Document Annotation

We present new results of our research on integration of ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning)(1) - a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive uncertain knowledge representation framework called ANUIC (Adaptive Net of Universally Interrelated Concepts). We discuss our recent achievements in taxonomy acquisition and show how even simple application of the principles of ANUIC can improve the results of initial knowledge extraction methods. We also suggest an algorithm for large-scale automatic annotation of natural language documents, applying uncertain knowledge bases created using our approach.