Global citation recommendation using knowledge graphs

Ayala-Gómez, F and Daróczy, Bálint Zoltán and Benczúr, András, ifj and Mathioudakis, M and Gionis, A (2018) Global citation recommendation using knowledge graphs. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 34 (5). pp. 3089-3100. ISSN 1064-1246 10.3233/JIFS-169493

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Informatics Laboratory
SWORD Depositor: MTMT Injector
Depositing User: MTMT Injector
Date Deposited: 08 Jan 2019 15:55
Last Modified: 08 Jan 2019 15:55

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