- (Lacoste-Julien et al., 2013) ⇒ Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, and Zoubin Ghahramani. (2013). “SIGMa: Simple Greedy Matching for Aligning Large Knowledge Bases.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487592
- Alignment; data mapping; entity; greedy algorithm; information networks; knowledge base; large-scale; relationship; semantic networks
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm that leverages both the structural information from the relationship graph and flexible similarity measures between entity properties in a greedy local search, which makes it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high accuracy. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.
|2013 SIGMaSimpleGreedyMatchingforAli||Simon Lacoste-Julien|
|SIGMa: Simple Greedy Matching for Aligning Large Knowledge Bases||10.1145/2487575.2487592||2013|
|Author||Simon Lacoste-Julien +, Konstantina Palla +, Alex Davies +, Gjergji Kasneci +, Thore Graepel + and Zoubin Ghahramani +|
|proceedings||Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||SIGMa: Simple Greedy Matching for Aligning Large Knowledge Bases +|