Name
Abstract | ||
|---|---|---|
Links are important for the publication of RDF data on the web. Yet, establishing links between data sets is not an easy task. We develop an approach for that purpose which extracts weak linkkeys. Linkkeys extend the notion of a key to the case of different data sets. They are made of a set of pairs of properties belonging to two different classes. A weak linkkey holds between two classes if any resources having common values for all of these properties are the same resources. An algorithm is proposed to generate a small set of candidate linkkeys. Depending on whether some of the, valid or invalid, links are known, we define supervised and non supervised measures for selecting the appropriate linkkeys. The supervised measures approximate precision and recall, while the non supervised measures are the ratio of pairs of entities a linkkey covers (coverage), and the ratio of entities from the same data set it identifies (discrimination). We have experimented these techniques on two data sets, showing the accuracy and robustness of both approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-419-0-15 | Frontiers in Artificial Intelligence and Applications |
DocType | Volume | ISSN |
Conference | 263 | 0922-6389 |
Citations | PageRank | References |
7 | 0.50 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Atencia | 1 | 88 | 10.79 |
| Jérôme David | 2 | 220 | 18.27 |
| Jérôme Euzenat | 3 | 2924 | 230.00 |