Name
Abstract | ||
|---|---|---|
The availability of large collections of linked data that can be accessed through public services and search endpoints requires methods and techniques for reducing the data complexity and providing high-level views of data contents defined according to users specific needs. To this end, a crucial step is the definition of data classification methods and techniques for the thematic aggregation of linked data. In this paper, we propose matching and clustering techniques specifically conceived for linked data classification, by focusing on the high level of heterogeneity of data descriptions in terms of the number and kind of their descriptive features. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2457317.2457330 | EDBT/ICDT Workshops |
Keywords | DocType | Citations |
high-level view,data classification,linked data classification,data content,data complexity,data classification method,feature-based approach,crucial step,clustering technique,descriptive feature,high level,data description | Conference | 13 |
PageRank | References | Authors |
0.84 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alfio Ferrara | 1 | 710 | 59.86 |
| Lorenzo Genta | 2 | 27 | 3.93 |
| Stefano Montanelli | 3 | 422 | 42.17 |