Fast entity desambiguation in large-scale databases

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Evaluating the work of a researcher, the impact of an academic institution, or the effectiveness of a research policy requires the analysis of the corresponding output in terms of scientific publications. The global scientific production represents a huge amount of information which can be accessed through several databases, but the presence of ambiguous information and the large size of the databases make it difficult to extract accurate results in a reasonable lapse of time. So far, an important part of the processing, known as disambiguation, must still be performed manually in order to achieve satisfactory results. In the past few months, a preliminary investigation of the properties of a new approach to automatic entity disambiguation was carried out within the framework of a NSERC ENGAGE project, in collaboration with industrial partner Science-Metrix. This new approach is based on a technique used in image processing for face detection in image sequences. One of its salient characteristic is the use of a cascaded classification algorithm in which the choice and ordering of classification features are critical to the derivation of a fast and accurate algorithm.