Abstract
In this paper, we propose a Relational Knowledge Transfer (RKT) model to predict Chinese ancient emperors’ personalities. It make good use of relational history semantic knowledge including Chinese emperors’ self-claimed character “朕”, statistics knowledge about correlation coefficient, and psychological knowledge including personality scores having normal distribution, to produce the virtual training datasets. With the Domain-Adversarial Training of Neural Network’s learning ability, we transferred human knowledge to the machine well with little manual data annotation effort. Compared with original version of PAP-TL tool, latest version of RKT model achieved the state of art in terms of the prediction accuracy of the Big Five personality scores of ancient Chinese emperors, with RMSE on average decreased from 9.16 to 5.40 (by 41%). This RKT model can be further applied to predict the personalities of Chinese of a specific group or period in the more than 2000 years ancient history.
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Xiao, H., Xing, F., Fan, M., Li, H., Zhu, T. (2022). Predicting Personalities of Ancient Chinese Emperors Based on Relational Knowledge Transfer Model. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_7
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