Title :
Self-evolution in knowledge bases
Author :
Barlas, Irtaza ; Ginart, Antonio ; Dorrity, Jordan L.
Author_Institution :
Impact Technol., LLC, Atlanta, GA
Abstract :
From the volumes of data that can be obtained today, information extraction has been a very challenging task. An organized set of information such that it can be considered as knowledge is yet another level of abstraction that puts these pieces of information in place, in space and time, so that when combined, they make sense, thus forming what is commonly known as a Knowledgebase (KB). A ´self-evolution´ process in a KB is meant to handle such information related issues as incorrect information, missing information, and incomplete information. Other maintenance issues are related to data organization that results in incorrect or inefficient information retrieval, and removal of unnecessary data. The concepts presented in this paper are inspired by the overall vision for an asset readiness decision-making system
Keywords :
automatic testing; data acquisition; knowledge based systems; data organization; decision making system; information retrieval; knowledge base; self-evolution; Data mining; Databases; Decision making; Information retrieval; Logistics; Prognostics and health management; Real time systems; Robustness; Scheduling; Uncertainty;
Conference_Titel :
Autotestcon, 2005. IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9101-2
DOI :
10.1109/AUTEST.2005.1609152
