Abstract. In this document we describe our approach to a specific subtask of ontology population, the extraction of instances of relations. We present a generic approach with which we are able to extract information from documents on the Web. The method exploits redundancy of information to compensate for loss of precision caused by the use of domain independent extraction methods. In this paper, we present the general approach and describe our implementation for a specific relation instance extraction task in the art domain. For this task, we describe experiments, discuss evaluation measures and present the results.
Abstract:To automatically classify and process web pages, current systems use the textual content of those pages, including both the displayed content and the underlying (HTML) code. However, a very important feature of a web page is its visual appearance. In this paper, we show that using generic visual features we can classify the web pages for several different types of tasks. The features used in this document are simple color and edge histograms, Gabor and texture features. These were extracted using an off-the-shelf visual feature extraction method. In three experiments, we classify web pages based on their aesthetic value, their recency and the type of website. Results show that these simple, global visual features already produce good classification results. We also introduce an online tool that uses the trained classifiers to assess new web pages.
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