Incompletely and Imprecisely Speaking: Using Dynamic Ontologies for Representing and Retrieving Information

We report on an approach to representation and retrieval of information from large textual databases. Our approach is based on dynamic ontologies that are automatically constructed from textual data by a new method combining techniques from knowledge representation, natural language processing, and machine learning. The method learns concepts automatically from documents, and uses them to build domain-speciic ontologies and to organize the information contained in the documents. The ontologies generated are dynamic in that they are constantly updated and expanded as new documents are added, requiring minimal supervision from domain experts. Information contained in the documents are eeciently retrieved based on concepts in the ontology, allowing for precision and completeness to be traded oo. A prototype implementation has been very encouraging.