TY - GEN
T1 - Citation Recommendation for Research Papers via Knowledge Graphs
AU - Brack, Arthur
AU - Hoppe, Anett
AU - Ewerth, Ralph
PY - 2021
Y1 - 2021
N2 - Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KGs) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results.
AB - Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KGs) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results.
KW - Information retrieval
KW - Research knowledge graph
KW - Research paper citation recommendation
UR - http://www.scopus.com/inward/record.url?scp=85115306611&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2106.05633
DO - 10.48550/arXiv.2106.05633
M3 - Conference contribution
AN - SCOPUS:85115306611
SN - 9783030863234
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 165
EP - 174
BT - Linking Theory and Practice of Digital Libraries
A2 - Berget, Gerd
A2 - Hall, Mark Michael
A2 - Brenn, Daniel
A2 - Kumpulainen, Sanna
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Theory and Practice of Digital Libraries, TPDL 2021
Y2 - 13 September 2021 through 17 September 2021
ER -