Zhang Yu, Wang Min, Saberi Morteza, Chang Elizabeth
School of Business, University of New South Wales, Canberra, ACT, Australia.
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
Front Big Data. 2019 Oct 31;2:38. doi: 10.3389/fdata.2019.00038. eCollection 2019.
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.
科学文章的数量迅速增长，为理解和识别研究问题以及最新的解决方案提供了科学依据。尽管解决问题的信息具有相当重要的意义，但现有的学术推荐系统缺乏从科学文章中检索此类信息以生成知识库并提供解决问题建议的能力。为了解决这个问题，本文提出了一个新颖的框架，用于构建面向解决方案的知识库并提供解决给定研究问题的建议。该框架由三个模块组成：一个基于语义的信息提取模块，用于从海量学术论文中挖掘研究问题和解决方案；一个基于异构文献计量图和排序算法的知识评估模块；以及一个知识库生成模块，用于生成带有建议的面向解决方案的地图。基于该框架，实现了一个原型学术解决方案支持系统。在入侵检测研究领域进行了案例研究，结果证明了所提方法的有效性和效率。
