activity
20192025
most citedTrans4E: Link Prediction on Scholarly Knowledge Graphs

61 citations · 99 across the 11 of their papers we have counts for

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11 papers · 1 filter

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.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…