Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Automatically refining the wikipedia infobox ontology
Proceedings of the 17th international conference on World Wide Web
Media Meets Semantic Web --- How the BBC Uses DBpedia and Linked Data to Make Connections
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Scaling textual inference to the web
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
On how to perform a gold standard based evaluation of ontology learning
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
ourSpaces: design and deployment of a semantic virtual research environment
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
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Tagging has become a wide-spread tool for organising content, from photos and music, to research paper and data-visualisations. Organising tags in a taxonomy adds hierarchical structure and relationships, this can be helpful, both for finding and applying tags to new content, as well as for enabling query expansion when searching. However, taxonomies can be very time-consuming to create and maintain. If a hierarchical taxonomy could be automatically built and adapted to a particular domain, the entry cost for using taxonomies for structuring information would go down. Small and medium enterprises (SMEs) do not currently have sufficient resources to invest in Enterprise 2.0 technologies like taxonomies, wikis or blogging as the entry cost it too high. The OrganiK project aims to make Enterprise 2.0 features available with low entry- and maintenance costs. In this paper, an algorithm and methodology to automatically create and maintain taxonomies is presented. It analyses enterprise document corpora and uses background information from domain-specific data sources or from the Linked Open Data cloud to improve and contextualise the created SKOS taxonomy. Content created in a Drupal-based Enterprise 2.0 content management system is automatically categorised, and the automatically created taxonomy is extended when needed. The system has been tested with corpora of medical abstracts, computer science papers, and the Enron email collection, and is in productive use.