Abstract
The volume of scientific articles grow rapidly, producing a scientific basis for understanding and identifying the research problems and the state-of-the-art solutions. Despite the considerable significance of the problem-solving information, existing scholarly recommending systems lack the ability to retrieve this information from the scientific articles for generating knowledge repositories and providing problem-solving recommendations. To address this issue, this paper proposes a novel framework to build solution-oriented knowledge repositories and provide recommendations to solve given research problems. The framework consists of three modules: a semantics based information extraction module mining research problems and solutions from massive academic papers; a knowledge assessment module based on the heterogeneous bibliometric graph and a ranking algorithm; and a knowledge repository generation module to produce solution-oriented maps with recommendations. Based on the framework, a prototype scholarly solution support system is implemented. A case study is carried out in the research field of intrusion detection, and the results demonstrate the effectiveness and efficiency of the proposed method.
Original language | English |
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Article number | 38 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Frontiers in Big Data |
Volume | 2 |
DOIs | |
Publication status | Published - 31 Oct 2019 |
Externally published | Yes |