Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Convex Optimization
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning domain ontologies for Web service descriptions: an experiment in bioinformatics
WWW '05 Proceedings of the 14th international conference on World Wide Web
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Ontology generation for large email collections
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
Hierarchical tag visualization and application for tag recommendations
Proceedings of the 20th ACM international conference on Information and knowledge management
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Ontology learning is an important task in Artificial Intelligence, Semantic Web and Text Mining. This paper presents a novel framework for, and solutions to, three practical problems in ontology learning. An incremental clustering approach is used to solve the problem of unknown group names. Learned models at each level of an ontology address the problem of no control over concept abstractness. A metric learning module moves beyond the limitation of traditional use of features and incorporates heterogeneous semantic evidence into the learning process. The metric-based learning framework integrates these separate components into a single, unified solution. An extensive evaluation with WordNet and Open Directory Project data demonstrates that the method is more effective than a state-of-the-art baseline algorithm.