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Enrichment of Academic Search Engine Results Pages by Citation-Based Graphs

机译:通过基于引文的图丰富学术搜索引擎结果页面

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Researchers' readings of academic papers make their research more sophisticated and objective. In this paper, we describe a method of supporting scholarly surveys by incorporating a graph based on citation relationships into the results page of an academic search engine. Conventional academic search engines have a problem in that users have difficulty in determining which academic papers are relevant to their needs because it is hard to understand the relationship between the academic papers that appear in the search results pages. Our method helps users to make judgments about the relevance of papers by clearly visualizing the relationship. It visualizes not only academic papers on the results page but also papers that have a strong citation relationship with them. We carefully considered the method of visualization and implemented a prototype with which we conducted a user study simulating scholarly surveys. We confirmed that our method improved the efficiency of scholarly surveys through the user study.
机译:研究人员对学术论文的阅读使他们的研究更加复杂和客观。在本文中,我们描述了一种通过将基于引文关系的图形合并到学术搜索引擎的结果页面中来支持学术调查的方法。常规的学术搜索引擎的问题在于,由于难以理解出现在搜索结果页面中的学术论文之间的关系,因此用户难以确定哪些学术论文与他们的需求相关。我们的方法可以通过清晰地可视化关系来帮助用户判断论文的相关性。它不仅可以在结果页面上形象化学术论文,还可以将与它们有强烈引用关系的论文可视化。我们仔细考虑了可视化方法,并实现了一个原型,通过该原型我们进行了用户研究,以模拟学术调查。我们确认,通过用户研究,我们的方法提高了学术调查的效率。

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