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| Research on Constructing an Academic Knowledge Graph of Multi-dimensional Knowledge Elements in Academic Full Texts |
| Shen Si1,2, Zhu Yufei1 |
1.School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094
2.China Center for International Economic Exchanges, Beijing 100050 |
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Abstract Academic texts contain a large amount of knowledge element information. Mining and organizing these knowledge elements can effectively improve the utilization efficiency of academic resources. Through the construction of an academic knowledge graph, connecting all kinds of tacit “knowledge elements” in an article can not only save time for scholars seeking to obtain knowledge points but also help them expand knowledge points through the network community in a knowledge graph. Through literature research and other methods, beginning with three dimensions, this paper determines the key knowledge elements in 18 academic papers, takes the text description information of knowledge elements as the entity object, and outlines the conceptual framework of an academic knowledge graph. Then, 515 pieces of literature in the JASIST are selected to study the manual annotation and entity extraction of the key knowledge elements in each paper based on deep learning. The research content includes whether such knowledge elements will create problems in the process of manual annotation and whether they will reach the expected value in automatic extraction when attempting to screen the knowledge elements involved in the construction of a knowledge graph. Finally, nine kinds of knowledge elements are selected, including mathematical formulas, software tools, data sources, specific models, tables, graphs, research prospects, research problems, and research results. Together with the titular data, triads composed of head entities, relations, and tail entities are generated and stored in the graph database for visual evaluation. Finally, the visualization and knowledge element retrieval of the graph are studied to prove its feasibility and scalability. The research shows that some knowledge elements in the text are suitable for large-scale automatic annotation, and all kinds of knowledge elements can form a dense knowledge community through mutual links.
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Received: 04 May 2023
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