Implementing a semantic interpreter using conceptual graphs
IBM Journal of Research and Development
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Conceptual Graphs: Draft Proposed American National Standard
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Detecting Deviations in Text Collections: An Approach Using Conceptual Graphs
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
From a children's first dictionary to a lexical knowledge base of conceptual graphs
From a children's first dictionary to a lexical knowledge base of conceptual graphs
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining Generalized Associations of Semantic Relations from Textual Web Content
IEEE Transactions on Knowledge and Data Engineering
Interlinguas: a classical approach for the semantic web. a practical case
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
On the need to bootstrap ontology learning with extraction grammar learning
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
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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.