Global citation recommendation using knowledge graphs

Simple item page
dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorAyala-Gomez, Fredericken_US
dc.contributor.authorDaroczy, Balinten_US
dc.contributor.authorBenczur, Andrasen_US
dc.contributor.authorMathioudakis, Michaelen_US
dc.contributor.authorGionis, Aristidesen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.organizationEötvös Loránd Universityen_US
dc.contributor.organizationHungarian Academy of Sciencesen_US
dc.date.accessioned2019-05-06T09:27:58Z
dc.date.available2019-05-06T09:27:58Z
dc.date.issued2018en_US
dc.description| openaire: EC/H2020/654024/EU//SoBigData
dc.description.abstractScholarly 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.en
dc.description.versionPeer revieweden
dc.format.extent12
dc.format.extent3089-3100
dc.identifier.citationAyala-Gomez, F, Daroczy, B, Benczur, A, Mathioudakis, M & Gionis, A 2018, ' Global citation recommendation using knowledge graphs ', Journal of Intelligent and Fuzzy Systems, vol. 34, no. 5, pp. 3089-3100 . https://doi.org/10.3233/JIFS-169493en
dc.identifier.doi10.3233/JIFS-169493en_US
dc.identifier.issn1064-1246
dc.identifier.otherPURE UUID: f15c8d95-e919-49ef-a224-821985c209caen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/f15c8d95-e919-49ef-a224-821985c209caen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/37802
dc.identifier.urnURN:NBN:fi:aalto-201905062919
dc.language.isoenen
dc.publisherIOS PRESS
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/654024/EU//SoBigDataen_US
dc.relation.ispartofseriesJournal of Intelligent and Fuzzy Systemsen
dc.relation.ispartofseriesVolume 34, issue 5en
dc.rightsrestrictedAccessen
dc.subject.keywordCitation recommendationsen_US
dc.subject.keywordknowledge graphsen_US
dc.subject.keywordrecommender systemsen_US
dc.titleGlobal citation recommendation using knowledge graphsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
Files
Collections
[article-cris] Perustieteiden korkeakoulu / SCI