Scenario-Driven Selection and Exploitation of Semantic Data for Optimal Named Entity Disambiguation

The rapidly increasing use of large-scale data on the Web has made named entity disambiguation a key research challenge in Information Extraction (IE) and development of the Semantic Web. In this paper we propose a novel disambiguation framework that utilizes background semantic information, typically in the form of Linked Data, to accurately determine the intended meaning of detected semantic entity references within texts. The novelty of our approach lies in the definition of a structured semi-automatic process that enables the custom selection and use of the semantic data that is optimal for the disambiguation scenario at hand. This process allows our framework to adapt to the particular characteristics of different domains and scenarios and, as experiments show, to be more effective than approaches primarily designed to work in open domain and unconstrained situations.