activity
20192026
most citedTrans4E: Link Prediction on Scholarly Knowledge Graphs

61 citations · 181 across the 28 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

cs.LG2019

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury +2

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal informatio…

cs.AI2019★ 2 cited

Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding

Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh +2

Knowledge graphs (KGs) represent world's facts in structured forms. KG completion exploits the existing facts in a KG to discover new ones. Translation-based embedding model (Trans…

cs.AI2019

LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules

Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1

Knowledge graph embedding models have gained significant attention in AI research. Recent works have shown that the inclusion of background knowledge, such as logical rules, can im…

cs.CL2019★ 4 cited

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…

cs.AI2019★ 9 cited

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…