  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 as 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 the 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.
