Title :
Finding and learning explanatory connections from scientific texts
Author :
Gomez, Fernando ; Segami, Carlos
Author_Institution :
Dept. of Comput. Sci., Univ. of Central Florida, Orlando, FL, USA
Abstract :
A theory for detecting and learning the explanatory connections between sentences in scientific texts is presented. A program called SNOWY that embodies the theory is also described. The knowledge in the program is organized around the notions of analytic and empirical knowledge. Analytic knowledge encompasses very general rules which are valid across any domain, while empirical knowledge includes rules whose validity is domain dependent. Examples of these rules and their representation are given
Keywords :
explanation; information analysis; knowledge based systems; knowledge representation; learning systems; SNOWY; analytic knowledge; empirical knowledge; explanatory connections; rule representation; scientific texts; sentences; Animal structures; Animation; Antibiotics; Birds; Joining processes; Knowledge representation; Marine animals; NASA; Snow; Tin;
Conference_Titel :
Tools for Artificial Intelligence, 1989. Architectures, Languages and Algorithms, IEEE International Workshop on
Conference_Location :
Fairfax, VA
Print_ISBN :
0-8186-1984-8
DOI :
10.1109/TAI.1989.65306
