2 citations · 3 across the 4 of their papers we have counts for
4 papers
Osmotic Learning: A Self-Supervised Paradigm for Decentralized Contextual Data Representation
Mario Colosi, Reza Farahani, Maria Fazio +2
Data within a specific context gains deeper significance beyond its isolated interpretation. In distributed systems, interdependent data sources reveal hidden relationships and lat…
Bi-View Embedding Fusion: A Hybrid Learning Approach for Knowledge Graph's Nodes Classification Addressing Problems with Limited Data
Rosario Napoli, Giovanni Lonia, Antonio Celesti +2
Traditional Machine Learning (ML) methods require large amounts of data to perform well, limiting their applicability in sparse or incomplete scenarios and forcing the usage of add…
Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread
Rosario Napoli, Gabriele Morabito, Antonio Celesti +2
The rise of graph-structured data has driven major advances in Graph Machine Learning (GML), where graph embeddings (GEs) map features from Knowledge Graphs (KGs) into vector space…
Unlocking Advanced Graph Machine Learning Insights through Knowledge Completion on Neo4j Graph Database
Rosario Napoli, Antonio Celesti, Massimo Villari +1
Graph Machine Learning (GML) with Graph Databases (GDBs) has gained significant relevance in recent years, due to its ability to handle complex interconnected data and apply ML tec…