Despite its outstanding performance in various graph tasks, vanilla Message Passing Neural
Network (MPNN) usually fails in link prediction tasks, as it only uses representations of two …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …

Learning a continuous dense low-dimensional representation of knowledge graphs (KGs),
known as knowledge graph embedding (KGE), has been viewed as the key to intelligent …

Various methods embed knowledge graphs with the goal of predicting missing edges.
Inference patterns are the logical relationships that occur in a graph. To make proper …

This study explores the use of Large Language Models (LLMs) for automatic evaluation of
knowledge graph (KG) completion models. Historically, validating information in KGs has …

Large language models (LLMs) have demonstrated remarkable proficiency in
understanding and generating responses to complex queries through large-scale pre …

We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge
graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the …

Abstract Graph Neural Networks (GNNs) have emerged as a state-of-the-art approach in
building modern Recommender Systems (RS). By leveraging the complex relationships …

This thesis is at the intersection of musicology and artificial intelligence, aiming to leverage
AI to help musicologists with repetitive work, such as object searching in the museum's …

Graph databases are the primary workhorse for storing and organizing structured
information over real-world entities. The core task on graph databases is query answering …