Name
Abstract | ||
|---|---|---|
Semantic aligning of heterogeneous geographical data from different sources behaves unsatisfactory on Geographical Semantic Web (GSW) due to the flat structure of GSW and the influence of spatial features. To solve this problem, this paper proposes a holistic framework for GSW aligning. This holistic framework firstly produces the initial matched results respectively for classes, properties and instances by the approval voting strategy, and then enhances these results by the mutual cooperating mechanism. Especially, spatial distance and spatial index are introduced to align instances and to improve the performance of aligning class and aligning property. To demonstrate its ability, this holistic framework is tested with two real GSWs. Compared with the state-of-the-art holistic alignment system, namely PARIS, this framework gains a large number of matched pairs. The Fl values of aligning class, aligning property and aligning instance respectively are 0.562, 0.545 and 0.646, all of which are higher than PARIS's. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3003464.3003465 | GIR |
Field | DocType | Citations |
Information integration,Data mining,Semantic integration,Information retrieval,Computer science,Flat organization,Semantic Web,Spatial database,Approval voting | Conference | 0 |
PageRank | References | Authors |
0.34 | 34 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Yu | 1 | 30 | 9.32 |
| Xiliang Liu | 2 | 166 | 13.32 |
| Mingxiao Li | 3 | 0 | 0.68 |
| Peng Peng | 4 | 19 | 12.21 |
| Feng Lu | 5 | 174 | 12.25 |