Acquisition of Scientific Literatures based on Citation-reason Visualization
Acquisition of Scientific Literatures based on Citation-reason Visualization
复制标题
基于引文原因可视化的科学文献获取
DOI：
10.5220/0005693801230130
复制
发表时间：
2016
期刊：
影响因子：
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通讯作者：
Ayato Inoue
中科院分区：
文献类型：
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作者：
Dongli Han;Hiroshi Koide;Ayato Inoue
在进行科学研究时，第一步是获取相关论文。通过在数字图书馆或在线搜索引擎中输入关键字，很容易抓取大量论文。然而，阅读所有检索到的论文，以找到最相关的是痛苦的时间消耗。以前的工作已经尝试通过基于参考关系的相互相似性对论文进行聚类来改进论文搜索，包括有限使用引用类型（例如提供背景与使用特定方法或数据）。然而，以前提出的方法只从一个角度对论文进行分类或组织，因此对于用户或上下文特定的需求不够灵活。此外，以前的作品都没有建立一个实际的系统的基础上的文件数据库。在本文中，我们首先从开放获取的论文源中建立论文数据库，然后使用机器学习自动预测论文之间每次引用的原因，最后将结果信息可视化在应用系统中，以帮助用户更有效地找到与其个人用途相关的论文。使用该系统的用户研究表明，我们的方法的有效性。
When carrying out scientific research, the first step is to acquire relevant papers. It is easy to grab vast numbers of papers by inputting a keyword into a digital library or an online search engine. However, reading all the retrieved papers to find the most relevant ones is agonizingly time-consuming. Previous works have tried to improve paper search by clustering papers with their mutual similarity based on reference relations, including limited use of the type of citation (e.g. providing background vs. using specific method or data). However, previously proposed methods only classify or organize the papers from one point of view, and hence not flexible enough for user or context-specific demands. Moreover, none of the previous works has built a practical system based on a paper database. In this paper, we first establish a paper database from an open-access paper source, then use machine learning to automatically predict the reason for each citation between papers, and finally visualize the resulting information in an application system to help users more efficiently find the papers relevant to their personal uses. User studies employing the system show the effectiveness of our approach.