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.

中文翻译：

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通过知识图谱对研究论文的引文推荐

研究论文的引文推荐是一项有价值的任务，可以通过建议相关的相关工作来帮助研究人员提高工作质量。此任务的当前方法主要依赖于论文的文本和引文网络。在本文中，我们建议利用额外的信息来源，即研究知识图（KG），它可以将基于上述科学概念的研究论文相互联系起来。我们的实验结果表明，将研究 KG 的信息与现有的最先进方法相结合是有益的。STM-KG（STM：科学、技术、医学）的实验结果是基于从十个领域的论文中提取的科学概念自动填充的知识图谱。