5 papers
An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs
Waleed Afandi, Hussein Abdallah, Ashraf Aboulnaga +1
Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expens…
Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs
Hussein Abdallah, Ibrahim Abdelaziz, Panos Kalnis +1
Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a closed world where answers mu…
CrediBench: Building Web-Scale Network Datasets for Information Integrity
Emma Kondrup, Sebastian Sabry, Hussein Abdallah +9
Automatically assessing the credibility of online sources presents an invaluable tool for navigating today's information ecosystem. However, existing approaches either depend on sc…
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
Hussein Abdallah, Waleed Afandi, Panos Kalnis +1
A Knowledge Graph (KG) is a heterogeneous graph encompassing a diverse range of node and edge types. Heterogeneous Graph Neural Networks (HGNNs) are popular for training machine le…
Towards a GML-Enabled Knowledge Graph Platform
Hussein Abdallah, Essam Mansour
This vision paper proposes KGNet, an on-demand graph machine learning (GML) as a service on top of RDF engines to support GML-enabled SPARQL queries. KGNet automates the training o…