Name
Abstract | ||
|---|---|---|
The datasets that are part of the Linking Open Data cloud diagramm (LOD cloud) are classied into the following topical categories: media, government, publications, life sciences, geographic, social networking, user-generated content, and cross-domain. The topical categories were manually assigned to the datasets. In this paper, we investigate to which extent the topical classication of new LOD datasets can be automated using machine learning techniques and the existing annotations as supervision. We conducted experiments with dierent classication techniques and dierent feature sets. The best classication |
Year | Venue | DocType |
|---|---|---|
2015 | LDOW@WWW | Conference |
Citations | PageRank | References |
3 | 0.39 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Meusel | 1 | 234 | 16.62 |
| Blerina Spahiu | 2 | 25 | 6.28 |
| Christian Bizer | 3 | 8448 | 524.93 |
| Heiko Paulheim | 4 | 1095 | 84.19 |