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We present a novel approach to learning taxonomic relations between terms by considering multiple and heterogeneous sources of evidence. In order to derive an optimal combination of these sources, we exploit a machine-learning approach , representing all the sources of evidence as first-order features and training standard classifiers. We consider in(More)
We present a novel approach to the automatic acquisition of taxonomic relations. The main difference to earlier approaches is that we do not only consider one single source of evidence, i.e. a specific algorithm or approach, but examine the possibility of learning taxonomic relations by considering various and heterogeneous forms of evidence. In particular,(More)
The tremendous success of the World Wide Web is countervailed by efforts needed to search and find relevant information. For tabular structures embedded in HTML documents typical keyword or link-analysis based search fails. The Semantic Web relies on annotating resources such as documents by means of ontologies and aims to overcome the bottleneck of finding(More)
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