4 papers
Structural Alignment in Link Prediction
Jeffrey Seathrún Sardina
While Knowledge Graphs (KGs) have become increasingly popular across various scientific disciplines for their ability to model and interlink huge quantities of data, essentially al…
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…
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…