Name
Abstract | ||
|---|---|---|
Event data is increasingly being represented according to the Linked Data principles. The need for large-scale machine learning on data represented in this format has thus led to the need for efficient approaches to compute RDF links between resources based on their temporal properties. Time-efficient approaches for computing links between RDF resources have been developed over the last years. However, dedicated approaches for linking resources based on temporal relations have been paid little attention to. In this paper, we address this research gap by presenting AEGLE, a novel approach for the efficient computation of links between events according to Allen's interval algebra. We study Allen's relations and show that we can reduce all thirteen relations to eight simpler relations. We then present an efficient algorithm with a complexity of O(n log n) for computing these eight relations. Our evaluation of the runtime of our algorithms shows that we outperform the state of the art by up to 4 orders of magnitude while maintaining a precision and a recall of 1. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-948 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Computer science,Artificial intelligence,Machine learning | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kleanthi Georgala | 1 | 6 | 2.12 |
| Mohamed Ahmed Sherif | 2 | 28 | 8.58 |
| Axel-Cyrille Ngonga Ngomo | 3 | 1775 | 139.40 |