Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A knowledge-based approach to organizing retrieved documents
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Mining topic-specific concepts and definitions on the web
WWW '03 Proceedings of the 12th international conference on World Wide Web
The TaxGen Framework: Automating the Generation of a Taxonomy for a Large Document Collection
HICSS '99 Proceedings of the Thirty-Second Annual Hawaii International Conference on System Sciences-Volume 2 - Volume 2
A practical web-based approach to generating topic hierarchy for text segments
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Demonstrating the semantic growbag: automatically creating topic facets for faceteddblp
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
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ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Recipes for semantic web dog food: the ESWC and ISWC metadata projects
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
The social bookmark and publication management system bibsonomy
The VLDB Journal — The International Journal on Very Large Data Bases
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ISWC'06 Proceedings of the 5th international conference on The Semantic Web
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ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the fine-grained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard.