Fast entity desambiguation in large-scale databases

评估研究人员的工作、学术机构的影响或研究政策的有效性，需要分析相应的科学出版物产出。全球科学生产代表了大量的信息，这些信息可以通过几个数据库访问，但是存在模糊的信息和数据库的庞大规模使得难以在合理的时间间隔内提取准确的结果。到目前为止，处理的一个重要部分，即消歧，仍然必须手动执行，以获得令人满意的结果。在过去的几个月里，在NSERC ENGAGE项目的框架内，与工业合作伙伴Science-Metrix合作，对自动实体消歧新方法的特性进行了初步调查。这种新方法是基于一种用于图像序列中人脸检测的图像处理技术。它的一个显著特点是使用级联分类算法，其中分类特征的选择和排序对推导快速准确的算法至关重要。

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.