NDLI: Graph-Based Knowledge Representation Model and Pattern Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Qiang Qu Jiangnan Qiu Chenyan Sun Yanzhang Wang
Copyright Year	2008
Description	Author affiliation: Sch. of Manage., Dalian Univ. of Technol., Dalian (Qiang Qu; Jiangnan Qiu)
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.
Starting Page	541
Ending Page	545
File Size	351763
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	reason Knowledge representation Conference management Information retrieval Knowledge management Knowledge Pattern Retrieval Sun text knowledge Technology management knowledge representation Character generation Production Context modeling Fuzzy systems
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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