Concept decompositions for large sparse text data using clustering
Machine Learning
Using text processing techniques to automatically enrich a domain ontology
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
A methodological approach to supporting organizational learning
International Journal of Human-Computer Studies
Towards the Semantic Web: Ontology-driven Knowledge Management
Towards the Semantic Web: Ontology-driven Knowledge Management
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A tolerance rough set approach to clustering web search results
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
A Semi-Supervised Document Clustering Algorithm Based on EM
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
ACORN: towards automating domain specific ontology construction process
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
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Designing mechanisms for creating concept ontologies automatically is an important research problem. In this work we have proposed a rough-set based mechanism to generate concept ontologies with concepts mined from documents. When the concept ontology is mined from preclassified documents, the output signifies the core set of domain concepts and their inter-relationships that define the categories, as well as the inter-category relationships. When the ontology is mined from a heterogeneous collection, the documents are first clustered into homogeneous groups and then mined for concepts. Rough set based lower and upper approximations have been used to identify core concepts and associated concepts for a domain or a group. The scheme has been tested over multiple domains.