TY - GEN
T1 - Text mining at detail level using conceptual graphs
AU - Montes-Y-gómez, Manuel
AU - Gelbukh, Alexander
AU - López-López, Aurelio
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
KW - Association discovery
KW - Conceptual clustering
KW - Conceptual graphs
KW - Deviation detection
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=84948977443&partnerID=8YFLogxK
U2 - 10.1007/3-540-45483-7_10
DO - 10.1007/3-540-45483-7_10
M3 - Contribución a la conferencia
SN - 3540439013
SN - 9783540439011
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 122
EP - 136
BT - Conceptual Structures
A2 - Priss, Uta
A2 - Corbett, Dan
A2 - Angelova, Galia
PB - Springer Verlag
T2 - 10th International Conference on Conceptual Structures, ICCS 2002
Y2 - 15 July 2002 through 19 July 2002
ER -