Global citation recommendation using knowledge graphs

Frederick Ayala-Gomez*, Balint Daroczy, Andras Benczur, Michael Mathioudakis, Aristides Gionis

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)


Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs - and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.

Original languageEnglish
Pages (from-to)3089-3100
Number of pages12
JournalJournal of Intelligent and Fuzzy Systems
Issue number5
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • Citation recommendations
  • knowledge graphs
  • recommender systems


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