Name
Abstract | ||
|---|---|---|
Considering that thousands if not millions of linked datasets will be published soon, we motivate in this paper the need for an ecient and eective way to rank interlinked datasets based on formal descriptions of their characteristics. We propose DING (from Dataset RankING) as a new approach to rank linked datasets using information provided by the voiD vocabulary. DING is a domain-independent link anal- ysis that measures the popularity of datasets by considering the cardinality and types of the relationships. We propose also a methodology to automatically assign weights to link types. We evaluate the proposed ranking algorithm against other well known ones, such as PageRank or HITS, using synthetic voiD descriptions. Early results show that DING performs better than the standard Web ranking algorithms. |
Year | Venue | DocType |
|---|---|---|
2009 | LDOW | Conference |
Citations | PageRank | References |
17 | 1.59 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nickolai Toupikov | 1 | 64 | 4.04 |
| Renaud Delbru | 2 | 945 | 66.40 |
| Michael Hausenblas | 3 | 478 | 52.35 |
| Giovanni Tummarello | 4 | 1106 | 104.63 |