Text mining with conceptual graphs

M. Montes-Y-Gómez; A. Gelbukh; A. López-López; R. Baeza-Yates

doi:10.1109/ICSMC.2001.973031

Text mining with conceptual graphs

M. Montes-Y-Gómez, A. Gelbukh, A. López-López, R. Baeza-Yates

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

A method for conceptual clustering of a collection of texts represented with conceptual graphs is presented. It uses the incremental strategy to construct the cluster hierarchy and incorporates some characteristics attractive for text mining processes. For instance, it considers the structural information of the graphs, uses domain knowledge to detect the cluster with generalized descriptions, and uses a user-defined similarity measure between the graphs.

Original language	English
Pages (from-to)	898-903
Number of pages	6
Journal	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	2
DOIs	https://doi.org/10.1109/ICSMC.2001.973031
State	Published - 2001
Externally published	Yes

Keywords

Conceptual clustering
Domain knowledge
Similarity measure
Summarization
Users interests profile

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10.1109/ICSMC.2001.973031

Cite this

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title = "Text mining with conceptual graphs",

abstract = "A method for conceptual clustering of a collection of texts represented with conceptual graphs is presented. It uses the incremental strategy to construct the cluster hierarchy and incorporates some characteristics attractive for text mining processes. For instance, it considers the structural information of the graphs, uses domain knowledge to detect the cluster with generalized descriptions, and uses a user-defined similarity measure between the graphs.",

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AU - Gelbukh, A.

AU - López-López, A.

AU - Baeza-Yates, R.

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AB - A method for conceptual clustering of a collection of texts represented with conceptual graphs is presented. It uses the incremental strategy to construct the cluster hierarchy and incorporates some characteristics attractive for text mining processes. For instance, it considers the structural information of the graphs, uses domain knowledge to detect the cluster with generalized descriptions, and uses a user-defined similarity measure between the graphs.

KW - Conceptual clustering

KW - Domain knowledge

KW - Similarity measure

KW - Summarization

KW - Users interests profile

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DO - 10.1109/ICSMC.2001.973031

M3 - Artículo

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EP - 903

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

ER -

Text mining with conceptual graphs

Abstract

Keywords

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