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@article{1757476,
author = {Mohamed, Sameh K and Novacek, Vít and Munoz, Emir and Muñoz, Emir},
article_location = {Switzerland},
article_number = {4},
doi = {http://dx.doi.org/10.3390/info12040147},
keywords = {loss functions; knowledge graph embeddings; link prediction},
language = {eng},
issn = {2078-2489},
journal = {Information},
title = {On Training Knowledge Graph Embedding Models},
url = {https://www.mdpi.com/2078-2489/12/4/147},
volume = {12},
year = {2021}
}
TY - JOUR
ID - 1757476
AU - Mohamed, Sameh K - Novacek, Vít - Munoz, Emir - Muñoz, Emir
PY - 2021
TI - On Training Knowledge Graph Embedding Models
JF - Information
VL - 12
IS - 4
SP - 147
EP - 147
PB - MDPI
SN - 20782489
KW - loss functions
KW - knowledge graph embeddings
KW - link prediction
UR - https://www.mdpi.com/2078-2489/12/4/147
N2 - Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models.
ER -
MOHAMED, Sameh K, Vít NOVACEK and Emir MUNOZ. On Training Knowledge Graph Embedding Models. \textit{Information}. Switzerland: MDPI, 2021, vol.~12, No~4, p.~147-165. ISSN~2078-2489. Available from: https://dx.doi.org/10.3390/info12040147.
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