Enhancing Talent Search by Integrating and Querying Big HR Data.

For companies, the need to efficiently deal with vast amounts of integrated multi-source data is becoming crucial. Core concerns are 1) proper and flexible human resources management approaches, for 2) more effective resource allocation, as well as 3) team staffing. We here propose to address the talent search problem. Our approach is based on professional skills characterization and normalization. In addition, to help in matching between unstructured documents (such as between resumes and job descriptions). To this end, we first provide a complete information technology skills taxonomy, together with a taxonomy managing companies and their sector of activity. This, in order to enhance named entity recognition and normalization. We next design a flexible, scalable and secure architecture integrating multi-source big data, which provides efficient unstructured document analysis and matching. Finally, we evaluate the performance of our platform using real data.