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A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals

A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals

Veröffentlicht: 2022

Journal: Scientometrics Journal

Seiten: 4887–4912
Verlag: Springer
Volume: 127

Referierte Veröffentlichung


Tags:Author Name DisambiguationBibliographic DataCitation DataClusteringKnowledge Graph EmbeddingsOpen Citations

Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github ( and Zenodo ( respectively.

Download: Media:s11192-022-04426-2.pdf
Weitere Informationen unter: Link
DOI Link: 10.1007/s11192-022-04426-2


Information Service Engineering