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| Multi-dimensional Analysis of Domain Knowledge Based on a Fine-grained Keyword Citation Network |
| Wang Jiamin1, Lu Wei2,3, Cheng Qikai2,3, Qin Chunxiu1 |
1.School of Economics and Management, Xidian University, Xi'an 710126
2.School of Information Management, Wuhan University, Wuhan 430072
3.Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan 430072 |
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Abstract Due to the lack of a semantic role of nodes and the association relationship between nodes being single in the current keyword citation network, this study enhances the semantic information of nodes and their association relationship through an academic text semantic function and proposes a fine-grained keyword citation network. First, this paper analyzes the content of academic text, extracts the keywords, citation associations, citation context, and citation objects of academic literature and identifies their term and citation functions. Next, the complex network method is utilized to construct a fine-grained keyword citation network. The multi-dimensional analysis of domain knowledge is carried out based on three aspects: citation function-aware subnet analysis, multi-dimensional association analysis of specific nodes, and fine-grained domain knowledge evolution analysis. The ACL domain dataset is taken as an example for empirical research. Results verify the validity of the proposed method; discover the patterns of use, extension, and comparison among domain knowledge; reveal the development of specific research problems or the application of specific methods; and describe the fine-grained evolution of domain knowledge. This study extends the research method and research depth of keyword networks and provides a new perspective and path for multi-dimensional analysis of domain knowledge.
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Received: 16 August 2021
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