Approximate subgraph matching
published: April 22, 2011, recorded: March 2011, views: 3760
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The paper presents an approach to detection of topic variations based on approximate graph matching. Text items are represented as semantic graphs and approximately matched based on a taxonomy of node and edge labels. Best-matching subgraphs are used as a template against which to align and compare the articles. The proposed approach is applied on news stories using WordNet as the predefined taxonomy. Illustrative experiments on real-world data show that the approach is promising.
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