DocumentCode :
3656941
Title :
Use of background knowledge in natural language understanding for information fusion
Author :
Stuart C. Shapiro;Daniel R. Schlegel
Author_Institution :
Department of Computer Science and Engineering, Center for Multisource Information Fusion and Center for Cognitive Science University at Buffalo, Buffalo, New York
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
901
Lastpage :
907
Abstract :
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.
Keywords :
"Agricultural machinery","Syntactics","Semantics","Logic gates","Organizations","Vehicles","Natural languages"
Publisher :
ieee
Conference_Titel :
Information Fusion (Fusion), 2015 18th International Conference on
Type :
conf
Filename :
7266655
Link To Document :
بازگشت