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Semantic representation of scientific documents for the e‐science Knowledge Grid
Author(s) -
Luo Xiangfeng,
Fang Ning,
Hu Bo,
Yan Kai,
Xiao Huizhe
Publication year - 2008
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1271
Subject(s) - computer science , merge (version control) , information retrieval , representation (politics) , knowledge representation and reasoning , artificial intelligence , politics , political science , law
Abstract Semantic representation of a topic is the basic element representation of a scientific document, which can be reflected by keywords' relations and their weights, as well as the state values of co‐occurring keywords in a documental fragment in which a topic is discussed. Concept lattice and probability statistical methods are proposed to generate fuzzy cognitive maps (FCMs) as the original semantic representation of documental fragments ( SRDFs ) for the e‐science Knowledge Grid. The additive capability of FCMs is used to merge SRDFs for reducing the noise and redundant information hidden in the original SRDFs . The loss of documental information in the merging process of SRDFs is measured by the proposed semantic merging entropy. Based on the distributional property of keywords on SRDFs , semantic distribution entropy is introduced to evaluate the change of keywords' distribution on SRDFs with the merging process. The semantic representation of a scientific document is automatically generated by the interconnection of the merged SRDFs belonging to one domain. The generating, merging and evaluating methods of the semantic representation of a scientific document are validated by experiments. The proposed semantic representation highlights the relations among keywords and topics, which reduces the complexity of document analysis in an e‐science Knowledge Grid effectively. Copyright © 2007 John Wiley & Sons, Ltd.