一种基于知识图谱的论文推荐方法：面向研究初学者
A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research Beginners
摘要：
从不同的学术数据库中搜索论文是研究初学者获得跨领域技术解决方案最常用的方法。然而，传统的搜索方法既没有考虑不同领域的知识异构性，也没有建立搜索的底层，包括但不限于目标解和待排除解的特征描述文本，因此效率低下，有时甚至是无用的。为了解决这一问题，本文提出了一种新的论文推荐方法，通过引入“主从式”领域知识图谱，不仅帮助用户更准确地表达自己的需求，而且帮助推荐系统更好地表达知识。具体而言，它不受冷启动问题的限制，是一种面向挑战的方法。为了验证该方法的合理性和有效性，我们选择了两个跨领域和三个不同的学术数据库进行验证。实验结果表明，该方法可以有效地帮助科研初学者在跨领域的情况下获取新的技术论文。在此基础上，提出了一种新的研究范式，为研究初学者在早期阶段。
英文摘要：
Searching for papers from different academic databases is the most commonly used method by research beginners to obtain cross-domain technical solutions. However, it is usually inefficient and sometimes even useless because traditional search methods neither consider knowledge heterogeneity in different domains nor build the bottom layer of search, including but not limited to the characteristic description text of target solutions and solutions to be excluded. To alleviate this problem, a novel paper recommendation method is proposed herein by introducing "master-slave" domain knowledge graphs, which not only help users express their requirements more accurately but also helps the recommendation system better express knowledge. Specifically, it is not restricted by the cold start problem and is a challenge-oriented method. To identify the rationality and usefulness of the proposed method, we selected two cross-domains and three different academic databases for verification. The experimental results demonstrate the feasibility of obtaining new technical papers in the cross-domain scenario by research beginners using the proposed method. Further, a new research paradigm for research beginners in the early stages is proposed herein.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
