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Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (resource description framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied to discover the underlying relation association patterns on RDF metadata. For pruning the large number of redundant overgeneralized patterns in relation pattern search space, the GP-Close algorithm adopts the notion of generalization closure for systematic overgeneralization reduction. The efficacy of our approach is demonstrated through empirical experiments conducted on an online database of terrorist activities
natural language processing, data mining, Internet,association rule mining, semantic relation, textual Web content, text mining, knowledge discovery, resource description framework metadata, natural language processing, WordNet, generalized association pattern mining algorithm, RDF mining,Data mining, Resource description framework, Text mining, Web sites, Natural language processing, Taxonomy, Databases, Association rules, Explosives, Information resources,text mining., RDF mining, association rule mining, relation association
"Mining Generalized Associations of Semantic Relations from Textual Web Content", IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 164-179, February 2007, doi:10.1109/TKDE.2007.36
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