Modeling context through domain ontologies
Information Retrieval
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A knowledge retrieval model using ontology mining and user profiling
Integrated Computer-Aided Engineering
Extremely fast text feature extraction for classification and indexing
Proceedings of the 17th ACM conference on Information and knowledge management
Classifying High-Dimensional Text and Web Data Using Very Short Patterns
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Effective multi-label active learning for text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The impact of document structure on keyphrase extraction
Proceedings of the 18th ACM conference on Information and knowledge management
Unsupervised relation extraction by mining Wikipedia texts using information from the web
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Ontology-based MEDLINE document classification
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining positive and negative patterns for relevance feature discovery
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Document clustering via dirichlet process mixture model with feature selection
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised transfer classification: application to text categorization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploit keyword query semantics and structure of data for effective XML keyword search
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Frequent itemset based hierarchical document clustering using Wikipedia as external knowledge
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Multi-modal summarization of key events and top players in sports tournament videos
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
A Personalized Ontology Model for Web Information Gathering
IEEE Transactions on Knowledge and Data Engineering
High-precision phrase-based document classification on a modern scale
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
Automated feature generation from structured knowledge
Proceedings of the 20th ACM international conference on Information and knowledge management
Effective Pattern Discovery for Text Mining
IEEE Transactions on Knowledge and Data Engineering
Satisfying Privacy Requirements Before Data Anonymization
The Computer Journal
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Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.