Name
Abstract | ||
|---|---|---|
We improve the state-of-the-art method for checking the consistency of large qualitative spatial networks that appear in the Web of Data by exploiting the scale-free-like structure observed in their constraint graphs. We propose an implementation scheme that triangulates the constraint graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them. We generate random scale-free-like qualitative spatial networks using the Barabasi-Albert (BA) model with a preferential attachment mechanism. We test our approach on the already existing random datasets that have been extensively used in the literature for evaluating the performance of qualitative spatial reasoners, our own generated random scale-free-like spatial networks, and real spatial datasets that have been made available as Linked Data. The analysis and experimental evaluation of our method presents significant improvements over the state-of-the-art approach, and establishes our implementation as the only possible solution to date to reason with large scale-free-like qualitative spatial networks efficiently. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1142/S0218213015500311 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Field | DocType | Volume |
Adjacency list,Data mining,Computer science,Constraint graph,Linked data,Theoretical computer science,Artificial intelligence,Machine learning,Preferential attachment,Hash table | Journal | 25 |
Issue | ISSN | Citations |
2 | 0218-2130 | 6 |
PageRank | References | Authors |
0.44 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Sioutis | 1 | 120 | 22.54 |
| Jean-françois Condotta | 2 | 255 | 23.20 |
| Manolis Koubarakis | 3 | 2790 | 322.32 |