Name
Abstract | ||
|---|---|---|
Knowledge graphs (KGs) represent world facts in a structured form. Although knowledge graphs are quantitatively huge and consist of millions of triples, the coverage is still only a small fraction of world's knowledge. Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embed... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533372 | 2021 International Joint Conference on Neural Networks (IJCNN) |
Keywords | DocType | ISSN |
Training,Costs,Computational modeling,Neural networks,Predictive models,Benchmark testing,Boosting | Conference | 2161-4393 |
ISBN | Citations | PageRank |
978-1-6654-3900-8 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengjin Xu | 1 | 2 | 3.10 |
| Mojtaba Nayyeri | 2 | 1 | 2.06 |
| Sahar Vahdati | 3 | 39 | 14.56 |
| Jens Lehmann | 4 | 5375 | 355.08 |