4 papers
Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
Knowledge Graphs (KGs) have seen increasing use across various domains -- from biomedicine and linguistics to general knowledge modelling. In order to facilitate the analysis of kn…
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied set…
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning
Jeffrey Sardina, Luca Costabello, Christophe Guéret
Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in…
TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
In this paper we introduce TWIG (Topologically-Weighted Intelligence Generation), a novel, embedding-free paradigm for simulating the output of KGEs that uses a tiny fraction of th…