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| Construction and Visual Analysis of Academic Paper-Linked Data Based on In-depth Mining |
| Qu Jiabin1,2,Ou Shiyan1,Ling Hongfei1 |
| School of Public Administration, Sichuan University, Chengdu 610064 |
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Abstract Since Linked Data was proposed, it has become the mainstream method of publishing structured data on the Web. With the rapid increase in linked data sets, the effective consumption and utilization of linked data has become the focus of researchers. This study intended to explore the mining and visual analysis of linked data. Firstly, we conducted in-depth mining of implicit information hidden in the metadata of academic papers in the geological field using text mining techniques. We then transformed the metadata and mined information into RDF-based semantic representation to construct the linked data of academic papers based on a newly designed “academic paper-scholar” ontology. On this basis, five visual analysis modules were designed to visualize the macro- and micro-knowledge of academic paper-linked data from multiple perspectives. The results showed that (1) the linked data constructed based on in-depth mining can deeply and comprehensively display knowledge hidden in the metadata of academic papers and (2) the visual analysis of linked data can intuitively present macro- and micro-knowledge in the form of graphics and thus facilitate users’ rapid consumption and utilization of linked data.
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Received: 13 November 2018
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