E. Spyromitros-Xioufis, S. Papadopoulos, A. Ginsca, A. Popescu, I. Kompatsiaris, I. Vlahavas, “Improving Diversity in Image Search via Supervised Relevance Scoring”, International Conference on Multimedia Retrieval, ACM, Shanghai, China, 2015.

Author(s): E. Papagiannopoulou, Grigorios Tsoumakas, Nick Bassiliades

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Appeared In: 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD 2015), Johanna Volker, Heiko Paulheim, Jens Lehmann, Vojtech Svatek (Ed.), CEUR Workshop Proceedings, Vol-1365, 2015.

Keywords: multi-label learning, linked open data, semantics, WordNet.

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Abstract: In multi-label learning, each instance can be related with one or more binary target variables. Multi-label learning problems are commonly found in many applications, e.g. in text classification where a news article is possible to be both on politics and finance. The main motivation of multi-label learning algorithms is the exploitation of label dependencies in order to improve prediction accuracy. In this paper, we present ongoing work on a method that uses the linked open data cloud to detect relationships between labels, enriches the set of labels with new concepts which are super classes of two or more labels, trains a model on the enhanced training set and finally, makes predictions on the enhanced test set in order to improve the prediction accuracy of the initial labels.