activity
20192022
most citedTrans4E: Link Prediction on Scholarly Knowledge Graphs

61 citations · 99 across the 6 of their papers we have counts for

collaborators

12 papers

cs.AI202223 cited

Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion

Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen +1

Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a g…

cs.AI202161 cited

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…

cs.AI2021

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…

cs.CL2020

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury +2

In the last few years, there has been a surge of interest in learning representations of entitiesand relations in knowledge graph (KG). However, the recent availability of temporal…

cs.LG2020

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…

cs.LG2020

Knowledge Graph Embeddings in Geometric Algebras

Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen +1

Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space. Existing KG embedding approaches model entities a…