A knowledge graph embeddings based approach for author name disambiguation using literals
Autor: | Cristian Santini, Genet Asefa Gesese, Silvio Peroni, Aldo Gangemi, Harald Sack, Mehwish Alam |
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Přispěvatelé: | Santini C., Gesese G.A., Peroni S., Gangemi A., Sack H., Alam M. |
Rok vydání: | 2022 |
Předmět: |
FOS: Computer and information sciences
Open citations Computer Science - Computation and Language Economics Computer Science - Artificial Intelligence Knowledge graph embeddings General Social Sciences Computer Science - Digital Libraries Citation data Library and Information Sciences Clustering Computer Science Applications Bibliographic data Artificial Intelligence (cs.AI) ddc:330 Knowledge graph embedding Digital Libraries (cs.DL) Author Name Disambiguation Computation and Language (cs.CL) |
Zdroj: | Scientometrics, 127 (8), 4887–4912 |
ISSN: | 1588-2861 0138-9130 |
Popis: | 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 (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively. |
Databáze: | OpenAIRE |
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