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| Method of Discovering Interdisciplinary Knowledge of the National Natural Science Foundation of China Based on Word Embedding: A Case Study on Artificial Intelligence and Information Management |
| Wang Weijun1,2, Yao Chang3, Qiao Ziyue1,2, Cui Wenjuan1, Du Yi1,2, Zhou Yuanchun1,2 |
1.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190
2.University of Chinese Academy of Sciences, Beijing 100049
3.Information Center, National Natural Science Foundation of China, Beijing 100085 |
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Abstract Interdisciplinary research is an important way to promote the resolution of various complex scientific problems. In this paper, the keyword co-occurrence relationship in the disciplines of artificial intelligence and information management in the projects funded by the National Natural Science Foundation of China is used to map the corresponding keywords to the low-dimensional vector space through word2vec correlation model. The keyword vector is used to calculate the relationship between keywords and obtain the quantized keyword co-occurrence relationship. PageRank algorithm is utilized to calculate the importance of keywords in the co-occurrence network. DBSCAN clustering algorithm is used to analyze keyword co-occurrence with an interdisciplinary nature that did not appear in the project, and textual information such as keyword importance is combined with visual information to analyze potential interdisciplinary knowledge. The experiment shows that the model proposed in this study can extract the potential interdisciplinary knowledge well and can filter and sort the interdisciplinary knowledge by using the interdisciplinary keyword co-occurrence relation. The results are interpretable and reasonable and provide new research ideas for exploring methods in discovering interdisciplinary knowledge and identifying its potential growth.
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Received: 13 August 2020
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