Name
Abstract | ||
|---|---|---|
Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.datak.2021.101943 | Data and Knowledge Engineering |
Keywords | DocType | Volume |
Data integration,Machine learning,Knowledge graphs | Journal | 137 |
Issue | ISSN | Citations |
1 | 0169-023X | 0 |
PageRank | References | Authors |
0.34 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Ayala | 1 | 0 | 0.34 |
| Inma Hernández | 2 | 0 | 0.34 |
| David Ruiz | 3 | 152 | 20.62 |
| Erhard Rahm | 4 | 7415 | 655.09 |