Paper Info
Title
Pattern discovery using semantic network analysis
Abstract
Cognitive information processing at higher conceptual levels requires a computational approach to knowledge representation and analysis. Semantic network analysis bridges the gap between probabilistic pattern recognition techniques and symbolic representations by replacing cumbersome and computationally complex forms of logic-based semantic inference common in symbolic approaches with mathematical metrics on graph representations of labelled, directed semantic networked data. These metrics in turn support assessment of evidentiary support for the presence of patterns of interest in which entities play specified roles in complex event scenarios. The resulting system allows patterns to be specified at higher levels of conceptual abstraction while also remaining robust to conflicting and incomplete information.
Year
DOI
Venue
2012
10.1109/CIP.2012.6232917
CIP
Keywords
Field
DocType
cognitive systems,formal logic,graph theory,knowledge representation,pattern recognition,cognitive information processing,complex event scenarios,computational approach,conceptual abstraction,directed semantic networked data,evidentiary support,graph representations,logic-based semantic inference,pattern discovery,probabilistic pattern recognition techniques,semantic network analysis,symbolic representations,graph representation,incomplete information,ontologies,data mining,information processing,measurement,databases,semantics,semantic network
Ontology (information science),Graph theory,Knowledge representation and reasoning,Inference,Computer science,Artificial intelligence,Natural language processing,Probabilistic logic,Semantics,Semantic computing,Complete information
Conference
ISBN
Citations 
PageRank 
978-1-4673-1877-8
0
0.34
References 
Authors
3
5
Name
Order
Citations
PageRank
robin burk100.34
Alan Chappell27712.16
Michelle Gregory312911.35
Cliff Joslyn411616.32
Liam R. McGrath581.19