Use of background knowledge in natural language understanding for information fusion

Tractor is a system for understanding English messages within the context of hard and soft information fusion for situation assessment. Tractor processes a message through text processors, and stores the result, expressed in a formal knowledge representation language, in a syntactic knowledge base. This knowledge base is enhanced with ontological and geographic information. Finally, Tractor applies hand-crafted syntax-semantics mapping rules to convert the enhanced syntactic knowledge base into a semantic knowledge base containing the information from the message enhanced with relevant background information. Throughout its processing, Tractor makes use of various kinds of background knowledge: knowledge of English usage; world knowledge; domain knowledge; and axiomatic knowledge. In this paper, we discuss the various kinds of background knowledge Tractor uses, and the roles they play in Tractor's understanding of the messages.