{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T22:34:58Z","timestamp":1770503698287,"version":"3.49.0"},"reference-count":38,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T00:00:00Z","timestamp":1527120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,5,24]]},"abstract":"<jats:p>\n                    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\n                    <jats:italic>citation recommendation<\/jats:italic>\n                    , 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 \u2013 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.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169493","type":"journal-article","created":{"date-parts":[[2018,5,25]],"date-time":"2018-05-25T05:02:58Z","timestamp":1527224578000},"page":"3089-3100","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":21,"title":["Global citation recommendation using\u00a0knowledge graphs"],"prefix":"10.1177","volume":"34","author":[{"given":"Frederick","family":"Ayala-G\u00f3mez","sequence":"first","affiliation":[{"name":"E\u00f6tv\u00f6s Lor\u00e1nd University, Faculty of Informatics, Budapest, Hungary"}]},{"given":"B\u00e1lint","family":"Dar\u00f3czy","sequence":"additional","affiliation":[{"name":"Inst. Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), Budapest, Hungary"}]},{"given":"Andr\u00e1s","family":"Bencz\u00far","sequence":"additional","affiliation":[{"name":"Inst. Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), Budapest, Hungary"}]},{"given":"Michael","family":"Mathioudakis","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, France"}]},{"given":"Aristides","family":"Gionis","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University, Espoo, Finland"}]}],"member":"179","published-online":{"date-parts":[[2018,5,24]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1629\/16191"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-010-0202-z"},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","unstructured":"HeQ. PeiJ. KiferD. MitraP. GilesL. Context-aware citation recommendation. 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