{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-language-citation-recommendation-via","title":"Cross-language Citation Recommendation via Hierarchical Representation Learning on Heterogeneous Graph","arxiv_id":"1812.11709","date":"2018-12-31","proceeding":null,"authors":["Zhuoren Jiang","Yue Yin","Liangcai Gao","Yao Lu","Xiaozhong Liu"],"abstract":"While the volume of scholarly publications has increased at a frenetic pace,\naccessing and consuming the useful candidate papers, in very large digital\nlibraries, is becoming an essential and challenging task for scholars.\nUnfortunately, because of language barrier, some scientists (especially the\njunior ones or graduate students who do not master other languages) cannot\nefficiently locate the publications hosted in a foreign language repository. In\nthis study, we propose a novel solution, cross-language citation recommendation\nvia Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to\naddress this new problem. HRLHG can learn a representation function by mapping\nthe publications, from multilingual repositories, to a low-dimensional joint\nembedding space from various kinds of vertexes and relations on a heterogeneous\ngraph. By leveraging both global (task specific) plus local (task independent)\ninformation as well as a novel supervised hierarchical random walk algorithm,\nthe proposed method can optimize the publication representations by maximizing\nthe likelihood of locating the important cross-language neighborhoods on the\ngraph. Experiment results show that the proposed method can not only outperform\nstate-of-the-art baseline models, but also improve the interpretability of the\nrepresentation model for cross-language citation recommendation task.","url_abs":"http://arxiv.org/abs/1812.11709v1","url_pdf":"http://arxiv.org/pdf/1812.11709v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cross-language-citation-recommendation-via","repo_url":"https://github.com/GraphEmbedding/HRLHG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"citation-recommendation","task_name":"Citation Recommendation"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}