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Feed Distillation Using AdaBoost and Topic Maps

Paper, was published by Andreas Lommatzsch, Christian Scheel, and Wai-Lung Lee at 2008-02-07

This paper gives some experiences with 'Feed Distillation' using Topic Maps.

External Link: download paper

In this paper, we want to retain our experiences by participating in TREC 2007 Blog Track ‘Feed Distillation’. To perform the run we combine various classifiers analyzing title-, content- and splog-specific features to predict the relevance of a feed related to a topic, based on the idea of AdaBoost. The implemented classifiers are based on keywords retrieved from different thesauri such as Wordnet and Wortschatz, as well as websites providing hierarchical organized ‘ontology’ such as the ‘Open Directory Project’ and Yahoo Directory. To structure the keywords, we use Topic Maps.

 

Topic Maps is an excellent paradigm to support human thinking and to visualize networked information. As part of my PhD project, I therefore chose Topic Maps as the conceptual foundation for designing and implementing a software prototype for semantic knowledge retrieval.

Stefan_smolnik
Stefan Smolnik
practical-semantics.com
Topic Maps Lab auf der Cebit 2011
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