The ENHANCE System: Creating Meaningful Sub-Types in a Database Knowledge Representation for Natural Language Generation

The knowledge representation is an important factor in natural language generation since it limits the semantic capabilities of the generation system. It is, however, a tedious task to hand code a knowledge representation which reflects both a user's view of a domain and the way that domain is modelled in the database. A system is presented which uses the contents of the database to form part of a database knowledge representation automatically. It augments a database schema depicting the database structure used for natural language generation. Computational solutions are presented for deriving the information types contained in the schema. Three types of world knowledge axioms are used to ensure that the representation formed is meaningful and contains salient information.