Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KG) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results.

翻译：研究论文的引文建议是一项宝贵的任务,通过提出相关工作的建议,可以帮助研究人员提高工作质量。目前这项工作的方法主要依靠论文和引证网络的文本。在本文中,我们提议利用另一个信息来源,即根据上述科学概念将研究论文相互连接的研究知识图(KG)。我们的实验结果表明,将研究KG的信息与现有最新方法相结合是有益的。 STM-KG(STM:科学、技术、医学)的实验结果为STM-KG(STM:科学、技术、医学)提供,STM-KG(STM:科学、技术、医学)是根据从十个域的论文中提取的科学概念自动成群的知识图。拟议的方法以平均20.6%(+0.8)的平均精确度超过艺术状态,用于获得的顶层50结果。