Paper Info
Title
Graph-Based Knowledge Representation Model and Pattern Retrieval
Abstract
Knowledge representation and pattern retrieval are the basis of knowledge discovery and reasoning. Different from many knowledge representation models such as production rules, graph model used to present context information in text has been envisioned as an appropriate solution to solve complex relevance more acceptably by the user. In this paper, a novel graph model, feature event dependency graph (FEDG) is proposed. FEDG emphasizes on representing the fact level knowledge compressively without losing important information. Meanwhile, based on this model, we propose retrieval and rank strategies for knowledge pattern retrieval which is meaningful for effective reasoning and latent knowledge discovery on large volumes of text knowledge. Extensive experiments on real knowledge sets, containing hundreds of domain specific rule based knowledge, demonstrate the feasibility and effectiveness of the proposed scheme.
Year
DOI
Venue
2008
10.1109/FSKD.2008.7
FSKD (5)
Keywords
Field
DocType
real knowledge set,knowledge representation model,fact level knowledge compressively,latent knowledge discovery,pattern recognition,reason,graph model,novel graph model,knowledge representation,feature event dependency graph,text knowledge,knowledge pattern retrieval,knowledge discovery,data mining,graph theory,graph-based knowledge representation model,text analysis,pattern retrieval,knowledge reasoning,rule based,pediatrics,feature extraction,production,cognition
Graph theory,Knowledge representation and reasoning,Pattern recognition,Computer science,Knowledge-based systems,Feature extraction,Artificial intelligence,Knowledge extraction,Knowledge base,Dependency graph,Machine learning,Open Knowledge Base Connectivity
Conference
Volume
ISBN
Citations 
5
978-0-7695-3305-6
3
PageRank 
References 
Authors
0.74
6
4
Name
Order
Citations
PageRank
qiang qu18312.15
Jiangnan Qiu2106.29
Chenyan Sun331.08
Yan-zhang Wang45919.56