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| Research on Domain Knowledge Alignment Based on Deep Learning: Knowledge Network Perspective |
| Yu Chuanming1, Li Haonan2, An Lu3 |
1.School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073
2.School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073
3.School of Information Management, Wuhan University, Wuhan 430072 |
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Abstract With the rapid development of big data, knowledge networks have become highly diverse and complex among different languages, domains, and modalities. How to align and integrate heterogeneous knowledge networks under multi-source context is becoming a considerable challenge. Based on the deep representation learning of knowledge networks, this paper proposes a knowledge network alignment (KNA) model, which consists of three modules—the knowledge network construction module, the cross-lingual network representation module, and the statistical machine learning module. To verify the validity of the model, we conducted an empirical study on Chinese and English bilingual knowledge networks and projected heterogeneous knowledge networks into the same space. Based on this process, the statistical machine learning model was designed by using known alignment links between knowledge nodes among different networks, and unknown alignment links were predicted by the model. The KNA model obtained a Precision@1 value (0.7731) in the cross-lingual word co-occurrence network alignment task, which is higher than that of the baseline method (0.6806), which verifies the validity of the KNA model in cross-lingual knowledge network alignment. The research results are of great significance for improving the accuracy of knowledge node alignment among different knowledge networks and for promoting the integration of heterogeneous knowledge networks under multi-source context.
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Received: 31 January 2019
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