Multi-feature and multi-relationship talent discovery algorithm based on knowledge graph

Abstract

In this era of information explosion, we need to query through scholar website, talent website or major recruitment websites to find the required talent information. However, there are problems of easy matching failure, low correlation, high maintenance cost, complicated steps and lack of information. Considering with the current research direction and its short comings, this paper proposes a multi-feature and multi-relationship talent discovery algorithm based on knowledge graph (TDKG). Firstly, the talent graph is constructed based on talent dataset, then the needs of user are analyzed by natural language processing, and finally the multi-feature and multi-relationship search is realized by combining the talent graph. By crawling the real talent data on the post graduate enrollment information website, the talent graph and the talent discovery system is constructed for verification. The experiment shows that this algorithm can precisely identify the needs of users and return the talent information required by users. Compared with the existing talent search methods, it has more pertinence, richer and more perfect functions.