DocumentCode :
3158020
Title :
Structural domain modeling for understanding equipment failure messages
Author :
Wauchope, Kenneth
Author_Institution :
US Naval Res. Lab., Washington, DC, USA
fYear :
1990
fDate :
1-4 Apr 1990
Firstpage :
188
Abstract :
Demonstrates that domain-specific knowledge is required to recover implicit references to causality from narrative texts that describe equipment failures. Understanding texts that discuss complex pieces of equipment requires the possession not only of general knowledge about the types of objects and predicates in the domain, but also detailed knowledge about the particular equipment in question. This more expert level of knowledge is needed to dereference the names and descriptions of equipment referred to in the text and to infer their causal relationships and operational states when these are only implicitly expressed by the message writer. Knowing the structural configuration of the equipment is useful in both tasks, and a structural domain model can be extracted readily from equipment manuals and their accompanying parts lists, thus easing the knowledge acquisition bottleneck problem and making practical applications more feasible
Keywords :
failure analysis; knowledge acquisition; knowledge based systems; mechanical engineering computing; natural languages; causal relationships; causality; description dereferencing; domain-specific knowledge; equipment failure messages; equipment manuals; implicit references; inference; knowledge acquisition bottleneck; name dereferencing; narrative texts; operational states; parts lists; structural configuration; structural domain modelling; text understanding; Artificial intelligence; Data mining; Data processing; Equipment failure; Failure analysis; Information analysis; Information retrieval; Marine vehicles; Military computing; Natural languages;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Southeastcon '90. Proceedings., IEEE
Conference_Location :
New Orleans, LA
Type :
conf
DOI :
10.1109/SECON.1990.117798
Filename :
117798
Link To Document :
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