Name
Abstract | ||
|---|---|---|
In recent years, Knowledge Graphs (KGs) have become ubiquitous, powering recommendation systems, natural language processing, and query answering, among others. Moreover, representation learning on graphs has reached unprecedentedly effective graph mining. In particular, Knowledge Graph Embedding (KGE) methods have gained increasing attention due to their effectiveness in representing real-world s... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/RTSI50628.2021.9597228 | 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) |
Keywords | DocType | ISBN |
Training,Industries,Runtime,Instruction sets,Random access memory,Predictive models,Natural language processing | Conference | 978-1-6654-4135-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angelica S. Valeriani | 1 | 0 | 0.34 |
| Guido Walter Di Donato | 2 | 0 | 0.68 |
| Marco D. Santambrogio | 3 | 771 | 91.15 |