From Big Scholarly Data to Solution-Oriented Knowledge Repository

Yu Zhang, Min Wang, Morteza Saberi, Elizabeth Chang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 languageEnglish
Article number38
Pages (from-to)1-10
Number of pages10
JournalFrontiers in Big Data
Publication statusPublished - 31 Oct 2019
Externally publishedYes


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