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Download Data Mining With Ontologies: Implementations, Findings and by Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel PDF

By Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel Hugo Xodo

Probably the most very important and hard difficulties in information mining is the definition of previous wisdom both from the method or the area. past wisdom is beneficial for choosing compatible information and mining ideas, pruning the distance of speculation, representing the output in a understandable method, and enhancing the general approach. info Mining with Ontologies: Implementations, Findings, and Frameworks presents a entire set of methodologies and instruments for the advance of ontological foundations for information mining in different domain names starting from biomedicine to advertising. Forming a benchmark reference for destiny efforts to augment functions in ontology usage and layout, this best Reference resource can be a useful addition to libraries all over the world.

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Natural Language Engineering, 8(4), 359-373. M. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103-134. , & Gauch, S. (1999). Ontologybased personalized search. In Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (pp. 391-398). Resnik, Ph. (2005). Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (pp.

Journal of Artificial Intelligence Research, 11, 95-130. , & Srinivasan, P. (1999). Hierarchical neural networks for text categorization. In Proceedings of the ACM’s Special Interest Group in Information Retrieval (SIGIR) Conference (pp. 281-282). C. (2004). A term weighting method based on lexical chain for automatic summarization. In Proceedings of the 5th Conference on Intelligent Text Processing and Computational Linguistics (CICLing) (pp. 636-639). , & Christodoulakis, D. (2005). Web directory construction using lexical chains.

Synonyms). The augmented elements of PCi and PCj respectively, are defined as: AugElements ( PCi ) = Ci  SynCi and AugElements ( PC j ) = C j  SynC j, where, SynCi denotes the set of the ontology’s concepts that are synonyms to any of the elements in Ci and SynCj denotes the set of the ontology’s concepts that are synonyms to any of the elements in Cj. The common elements between the augmented lexical chains PCi and PCj, are determined as: ComElements ( PCi , PC j ) = AugElementsi  AugElements j We formally define the problem of computing pages’ semantic similarities as follows: if pages Pi and Pj share elements in common, produce the correlation look up table with triples of the form .

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