Name
Abstract | ||
|---|---|---|
Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is difficult to know whether the links introduced improve or diminish the quality of Linked Data. In this paper, we present LINK-QA, an extensible framework that allows for the assessment of Linked Data mappings using network metrics. We test five metrics using this framework on a set of known good and bad links generated by a common mapping system, and show the behaviour of those metrics. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-30284-8_13 | ESWC |
Keywords | Field | DocType |
common mapping system,network metrics,extensible framework,extra information,enriched semantics,network measure,linked data,bad link,unsuitable network structure,automated approach,quality assurance,network analysis | Pointer (computer programming),Data mining,Data set,Computer science,Linked data,Network analysis,Extensibility,Database,Semantics,Metrics,Quality assurance | Conference |
Citations | PageRank | References |
59 | 2.46 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christophe Gueret | 1 | 179 | 19.16 |
| Paul Groth | 2 | 1709 | 139.30 |
| Claus Stadler | 3 | 363 | 26.65 |
| Jens Lehmann | 4 | 5375 | 355.08 |