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Content Provider | IEEE Xplore Digital Library |
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Author | Jiangning Wu Yanzhong Dang Donghua Pan Zhaoguo Xuan Qiaofeng Liu |
Copyright Year | 2010 |
Abstract | To represent the textual knowledge more expressively, a kind of semantic-based graph structure is proposed for this issue and thereafter applied to clustering problems. Such graph structure for textual representation consists of nodes and directed edges, which stand for the feature terms derived from the texts and the semantic relationships between them, respectively. Moreover, the weight is assigned to each edge so that the strength of relationship between two terms can be measured. For this weighted directed graph structure, a novel graph similarity algorithm is developed by extracting the maximum common subgraph between two concerned graphs, which can therefore be used to measure the distance between two graph structures, i.e. two texts, and finally be used to sort the texts into different clusters. Some experiments have been done through the proposed semantic graph structure in clustering applications and the results have proved the high performance of our textual knowledge representation model. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 345942 |
Page Count | 8 |
File Format | |
ISBN | 9781424455096 |
ISSN | 15301605 |
e-ISBN | 9781424455102 |
DOI | 10.1109/HICSS.2010.366 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-01-05 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Knowledge representation Clustering algorithms Knowledge engineering Systems engineering and theory Knowledge management Mathematical model Tree graphs Joining processes Mutual information Colon |
Content Type | Text |
Resource Type | Article |
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