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Text Mining at Detail Level Using Conceptual Graphs

by: Manuel Montes y Gómez, Alexander Gelbukh, and Aurelio López-López

In: Proc. DEXA-2002, Nr. 2393Springer-Verlag (2002) , p. 122-136.

Abstract

Text mining is defined as knowledge discovery in large text collections. It detects interesting patterns such as clusters, associations, deviations, similarities, and differences in sets of texts. Current text mining methods use simplistic representations of text contents, such as keyword vectors, which imply serious limitations on the kind and meaningfulness of possible discoveries. We show how to do some typical mining tasks using conceptual graphs as formal but meaningful representation of texts. Our methods involve qualitative and quantitative comparison of conceptual graphs, conceptual clustering, building a conceptual hierarchy, and application of data mining techniques to this hierarchy in order to detect interesting associations and deviations. Our experiments show that, despite widespread misbelief, detailed meaningful mining with conceptual graphs is computationally affordable.

BibTeX record

@inproceedings{Montes:2002,
  abstract = {Text mining is defined as knowledge discovery in large text collections. It detects interesting patterns such as clusters, associations, deviations, similarities, and differences in sets of texts. Current text mining methods use simplistic representations of text contents, such as keyword vectors, which imply serious limitations on the kind and meaningfulness of possible discoveries. We show how to do some typical mining tasks using conceptual graphs as formal but meaningful representation of texts. Our methods involve qualitative and quantitative comparison of conceptual graphs, conceptual clustering, building a conceptual hierarchy, and application of data mining techniques to this hierarchy in order to detect interesting associations and deviations. Our experiments show that, despite widespread misbelief, detailed meaningful mining with conceptual graphs is computationally affordable.},
  added-at = {2007-12-14T02:44:07.000+0100},
  author = {y G{\'o}mez, Manuel Montes and Gelbukh, Alexander and L{\'o}pez-L{\'o}pez, Aurelio},
  biburl = {http://www.bibsonomy.org/bibtex/275561b4bb59407a24c6af5d62fb4d04c/diego_ma},
  booktitle = {Proc. DEXA-2002},
  interhash = {e071c5cd86614ac00f388268973e245d},
  intrahash = {75561b4bb59407a24c6af5d62fb4d04c},
  keywords = {semantic_closeness},
  number = 2393,
  pages = {122-136},
  publisher = {Springer-Verlag},
  series = {Lecture Notes in Artificial Intelligence},
  timestamp = {2007-12-14T02:44:07.000+0100},
  title = {Text Mining at Detail Level Using Conceptual Graphs},
  year = 2002
}

Endnote record

%0 Conference Paper
%1 Montes:2002
%A y G{\'o}mez, Manuel Montes
%A Gelbukh, Alexander
%A L{\'o}pez-L{\'o}pez, Aurelio
%B Proc. DEXA-2002
%D 2002
%I Springer-Verlag
%K 
%N 2393
%P 122-136
%T Text Mining at Detail Level Using Conceptual Graphs
%X Text mining is defined as knowledge discovery in large text collections. It detects interesting patterns such as clusters, associations, deviations, similarities, and differences in sets of texts. Current text mining methods use simplistic representations of text contents, such as keyword vectors, which imply serious limitations on the kind and meaningfulness of possible discoveries. We show how to do some typical mining tasks using conceptual graphs as formal but meaningful representation of texts. Our methods involve qualitative and quantitative comparison of conceptual graphs, conceptual clustering, building a conceptual hierarchy, and application of data mining techniques to this hierarchy in order to detect interesting associations and deviations. Our experiments show that, despite widespread misbelief, detailed meaningful mining with conceptual graphs is computationally affordable.