While the volume of scholarly publications has increased at a frenetic pace,
accessing and consuming the useful candidate papers, in very large digital
libraries, is becoming an essential and challenging task for scholars.
Unfortunately, because of language barrier, some scientists (especially the
junior ones or graduate students who do not master other languages) cannot
efficiently locate the publications hosted in a foreign language repository. In
this study, we propose a novel solution, cross-language citation recommendation
via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to
address this new problem. HRLHG can learn a representation function by mapping
the publications, from multilingual repositories, to a low-dimensional joint
embedding space from various kinds of vertexes and relations on a heterogeneous
graph. By leveraging both global (task specific) plus local (task independent)
information as well as a novel supervised hierarchical random walk algorithm,
the proposed method can optimize the publication representations by maximizing
the likelihood of locating the important cross-language neighborhoods on the
graph. Experiment results show that the proposed method can not only outperform
state-of-the-art baseline models, but also improve the interpretability of the
representation model for cross-language citation recommendation task.

本研究提出了一种新颖的方案，通过异构图上的分层表示学习来进行跨语言引用推荐，可以优化出在图上定位重要的跨语言邻域的概率，实验结果显示该方法不仅可以超越现有的基线模型，而且可以提高跨语言引用推荐任务的表现。