Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Searching structured documents with the enhanced retrieval functionality of free WAIS-sf and SFgate
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Graph-based text database for knowledge discovery
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Measuring text similarity with dynamic time warping
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
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Knowledge discovery from a large volumes of texts usually requires many complex analysis steps. The graph-based text representation model has been proposed to simplify the steps. The model represents texts in a formal manner, Subject Graphs, and provides text handling operations whose inputs and outputs are identical in form, i.e. a set of subject graphs, so they can be combined in any order. A subject graph uses node weight to represent the significance of each term, and link weight to represent that of each term-term association. This paper concentrates on the algorithms for making subject graphs and calculating the similarity between them. An evaluation shows that Subject Graphs can calculate the similarity between texts more precisely than term vectors, since they incorporate the significance of association between terms.