%0 Book Section
%A Ceccarelli, Diego
%A Lucchese, Claudio
%A Orlando, Salvatore
%A Perego, Raffaele
%A Trani, Salvatore
%B Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
%D 2013
%F eprints:1890
%I ACM
%K Entity linking, relatedness measures, learning to rank
%P 139-148
%T Learning Relatedness Measures for Entity Linking
%U http://eprints.imtlucca.it/1890/
%X Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking.  The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms.