Name
Abstract | ||
|---|---|---|
Usually the content of the dataset published as LOD is rather unknown and data publishers have to deal with the challenge of interlinking new knowledge with existing datasets. Although there exist tools to facilitate data interlinking, they use prior knowledge about the datasets to be interlinked. In this paper we present a framework to profile the quality of owl:sameAs property in the Linked Open Data cloud and automatically discover new similarity links giving a similarity score for all the instances without prior knowledge about the properties used. Experimental results demonstrate the usefulness and effectiveness of the framework to automatically generate new links between two or more similar instances. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICDEW.2016.7495626 | 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) |
Field | DocType | Citations |
Data science,Data mining,Data collection,Computer science,Profiling (computer programming),Interoperability,Linked data,Database,Cloud computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Blerina Spahiu | 1 | 25 | 6.28 |
| Cheng Xie | 2 | 162 | 15.84 |
| Anisa Rula | 3 | 214 | 15.67 |
| Andrea Maurino | 4 | 736 | 53.87 |
| Hongming Cai | 5 | 396 | 58.68 |