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，需要从科学出版物的角度分析相应的产出。全球科学生产代表着可以通过几个数据库访问的海量信息，但由于存在不明确的信息和庞大的数据库，很难在合理的时间段内提取准确的结果。到目前为止，这一过程的一个重要部分，即消除歧义，仍然必须手动执行，才能取得令人满意的结果。在过去的几个月里，在NSERC Engage项目的框架内，与工业伙伴Science-Metrix合作，对一种新的自动实体歧义消除方法的性质进行了初步调查。这种新的方法是基于图像处理中用于图像序列中的人脸检测的技术。它的显著特征之一是使用级联分类算法，其中分类特征的选择和排序对于快速而准确的算法的推导至关重要。

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.