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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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