Word sense disambiguation and information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
User-Centred Ontology Learning for Knowledge Management
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Generating hierarchical summaries for web searches
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
CP/CV: concept similarity mining without frequency information from domain describing taxonomies
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Integrating Semantic Knowledge into Text Similarity and Information Retrieval
ICSC '07 Proceedings of the International Conference on Semantic Computing
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
CoSeNa: a context-based search and navigation system
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Editorial: Narrative-based taxonomy distillation for effective indexing of text collections
Data & Knowledge Engineering
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Taxonomies embody formalized knowledge and define aggregations between concepts/categories in a given domain, facilitating the organization of the data and making the contents easily accessible to the users. Since taxonomies have significant roles in the data annotation, search and navigation, they are often carefully engineered. However, especially in very dynamic content, they do not necessarily reflect the content knowledge. Thus, in this paper, we propose A Narrative Interpretation of Taxonomies for their Adaptation (ANITA) for re-structuring existing taxonomies to varying application contexts and we evaluate the proposed scheme by user studies that show that the proposed algorithm is able to adapt the taxonomy in a new compact and understandable structure from a human point of view.