61 citations · 76 across the 6 of their papers we have counts for
10 papers
Trans4E: Link Prediction on Scholarly Knowledge Graphs
Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati +8
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based services. In the scholarly domain, KGs describing research publications typically…
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati +1
Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embe…
Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations
Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1
Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space (usu…
5* Knowledge Graph Embeddings with Projective Transformations
Mojtaba Nayyeri, Sahar Vahdati, Can Aykul +1
Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that ma…
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati +2
Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings a…
Soft Marginal TransE for Scholarly Knowledge Graph Completion
Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann +1
Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link…