most citedLink Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models

27 citations · 30 across the 2 of their papers we have counts for

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cs.AI2025

Geometric Structural Knowledge Graph Foundation Model

Ling Xin, Mojtaba Nayyeri, Zahra Makki Nayeri +1

Structural knowledge graph foundation models aim to generalize reasoning to completely new graphs with unseen entities and relations. A key limitation of existing approaches like U…

cs.AI2025

Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning

Osama Mohammed, Jiaxin Pan, Mojtaba Nayyeri +2

Modeling evolving interactions among entities is critical in many real-world tasks. For example, predicting driver maneuvers in traffic requires tracking how neighboring vehicles a…

cs.AI2025

Towards Foundation Model on Temporal Knowledge Graph Reasoning

Jiaxin Pan, Mojtaba Nayyeri, Osama Mohammed +4

Temporal Knowledge Graphs (TKGs) store temporal facts with quadruple formats (s, p, o, t). Existing Temporal Knowledge Graph Embedding (TKGE) models perform link prediction tasks i…

cs.AI2024

Generating Ontologies via Knowledge Graph Query Embedding Learning

Yunjie He, Daniel Hernandez, Mojtaba Nayyeri +4

Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, r…

cs.AI2024

Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction

Yuqicheng Zhu, Nico Potyka, Mojtaba Nayyeri +4

Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link…

cs.AI20233 cited

Geometric Relational Embeddings: A Survey

Bo Xiong, Mojtaba Nayyeri, Ming Jin +4

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for str…