Augmenting 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. This paper identifies several information types in a knowledge representation that can be used to generate meaningful responses to questions about database structure. Creating such a knowledge representation, however, is a long and tedious process. A system is presented which uses the contents of the database to form part of this knowledge representation automatically. It employs three types of world knowledge axioms to ensure that the representation formed is meaningful and contains salient information. representation reflects both the database contents and the database designer's view of the world. One important class of questions involves comparing database entities. The system's knowledge representation must therefore contain meaningful information that can be used to make comparisons (analogies) between various entity classes. This paper focuses specifically on those aspects of the knowledge representation generated by ENHANCEwhich facilitate the use of analogies. An overview of the knowledge representation used by TEXT is first given. This is followed by a discussion of how part of this representation is automatically created by ENHANCE.