2 citations · 2 across the 2 of their papers we have counts for
4 papers
APOLO: Automatic Prompt Optimization for Ontology Learning
Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu +3
Ontology Learning (OL) from text has advanced with the emergence of Large Language Models (LLMs), but it remains challenging due to the limited availability of annotated training d…
Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing
Huu Tan Mai, Cuong Xuan Chu, Heiko Paulheim +1
Graph neural networks (GNNs) excel at representation learning on Knowledge Graphs (KGs), achieving stateof-the-art performance on tasks like link prediction or entity classificatio…
Explaining Graph Neural Networks for Node Similarity on Graphs
Daniel Daza, Cuong Xuan Chu, Trung-Kien Tran +3
Similarity search is a fundamental task for exploiting information in various applications dealing with graph data, such as citation networks or knowledge graphs. While this task h…
Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
Medina Andresel, Trung-Kien Tran, Csaba Domokos +2
Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the…