Paper Info
Title
Value-added treatment inference model for rule-based certainty knowledge
Abstract
During various knowledge sources and expert comments in the knowledge base may lead to knowledge overlaps, conflicts or data size variations in the knowledge base, with wrong knowledge leads to wrong decisions. This study proposes using an O-A-RV structure to express rule-based knowledge, integrating conditional probability, vector matrix and artificial intelligence, and building a conditional probability knowledge similarity algorithm, so as to obtain a similarity matrix of knowledge and determines correlations among knowledge. Also proposed to use reliability factor theory to express knowledge conflicts, overlaps and data size variations. Based on knowledge correlations, a rule-based knowledge value-added treatment inference algorithm is set up to run value-added treatments, so that wrong decisions can be avoided.
Year
DOI
Venue
2008
10.1016/j.eswa.2006.12.026
Expert Syst. Appl.
Keywords
Field
DocType
artificial intelligence,various knowledge source,conditional probability,knowledge overlap,rule-based knowledge,value-added treatment inference model,knowledge representation,knowledge correlation,wrong decision,knowledge conflict,data size variation,knowledge base,rule-based certainty knowledge,wrong knowledge,conditional probability knowledge similarity,value-added treatment,reliability factor,knowledge integration,artificial intelligent,rule based,value added
Data mining,Commonsense knowledge,Rule-based system,Knowledge representation and reasoning,Inference,Computer science,Knowledge-based systems,Model-based reasoning,Artificial intelligence,Knowledge base,Machine learning,Legal expert system
Journal
Volume
Issue
ISSN
34
2
Expert Systems With Applications
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Min-Yuan Cheng117419.84
Chin-Jung Huang2114.08