Title :
Tractor: A framework for soft information fusion
Author :
Prentice, M. ; Kandefer, M. ; Shapiro, S.C.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. at Buffalo, Buffalo, NY, USA
Abstract :
This paper presents a soft information fusion framework for creating a propositional graph from natural language messages with an emphasis on producing these graphs for fusion with other messages. The framework utilizes artificial intelligence techniques from natural language understanding, knowledge representation, and information retrieval.
Keywords :
graph theory; information retrieval; knowledge representation; natural language processing; sensor fusion; Tractor; artificial intelligence techniques; information retrieval; knowledge representation; natural language messages; natural language understanding; propositional graph; soft information fusion framework; Agricultural machinery; Context; Logic gates; Natural languages; Ontologies; Resource description framework; Syntactics; context; hard/soft data fusion; ontologies; propo-sitional graphs;
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
DOI :
10.1109/ICIF.2010.5711896
