Expert knowledge recommendation systems based on conceptual similarity and space mapping

Abstract

The semantic analysis method of structured big data generated based on human knowledge is important in expert recommendation systems and scientific and technological information analysis. In these fields, the most important problem is the calculation of concept similarity. The study aims to explore the spatial mapping relationship between the general knowledge base and the professional knowledge base for the application of the general knowledge map in professional fields. With the core resource database (CRD) as the main body of the general knowledge and the institutional repository (IR) as the main body of the professional knowledge, the conceptual features of institutional expert knowledge were firstly abstracted from IR and inferred from small-scale datasets and the mathematical model was established based on the similarity of text concepts and related ranking results. Then, a two-set concept space mapping algorithm between CRD and IR was designed. In the algorithm, the more granular concept nodes were extracted from the information on the shortest paths among concepts to obtain a new knowledge set, the Expert Knowledge Recommendation System (EKRS). Finally, the simulation experiment was carried out with open datasets to verify the algorithm. The simulation results showed that the algorithm reduced the structural complexity in the calculation of large datasets. The proposed system model had a clear knowledge structure and the recommended accuracy of the text similarity was high. For small-scale knowledge base datasets with different sparsity, the system showed the stable performance, indicating the better convergence and robustness of the algorithm.