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Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
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A semantic approach to contextual advertising
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ISWC '09 Proceedings of the 8th International Semantic Web Conference
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EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
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One of the main problems in online advertising is to display ads which are relevant and appropriate w.r.t. what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines. We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.