SIGMa: simple greedy matching for aligning large knowledge bases

By Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani. 2012

201 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Zhiyuan Liu
3. Maosong Sun
4. Jie Tang
5. Wei Hu
6. Zequn Sun
7. Lingbing Guo
8. Zhilin Yang

In Management Science and Operations Research

1. Wei Hu
2. Zequn Sun
3. Lingbing Guo
4. Zhiyuan Liu
5. Maosong Sun
6. Philip S. Yu
7. Ruobing Xie
8. Hao Zhu

In Graph Learning

1. Zequn Sun
2. Wei Hu
3. Zhiyuan Liu
4. Maosong Sun
5. Ruobing Xie
6. Jie Tang
7. Hao Zhu
8. Philip S. Yu

Citations in the corpus

• MMKG: Multi-Modal Knowledge Graphs (2019, unknown): “A large number of approaches of the entity matching literature have been evaluated as part of the Ontology Alignment Evaluation Initiative (OAEI) [1] using data sets such as YAGO, FREEBASE, and IMDB[16,28,22].”
• Iterative Entity Alignment via Joint Knowledge Embeddings (2017, methodology): “As for graph-based models, there are also many methods focusing on using heterogeneous information in different knowledge graphs for knowledge alignment. such as [Nguyen et al., 2011; Wang et al., 2013; Rinser et al., 2013; Lacoste-Julien et al., 2013].”
• COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency (2015, methodology): “Lacoste et al. [18] proposed a greedy method that successfully aligned two large-scale knowledge bases.”
• Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (2019, background): “…object; and (ii) KG completion , a.k.a. link prediction, which aims to complete the missing facts in a single KG. Conventional methods usually rank candidates by exploiting various features, as well as using crowdsourcing (Lacoste-Julien et al., 2013; Suchanek et al., 2012; Zhuang et al., 2017).”
• A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “The other is based on similarity computation, which compares symbolic features of entities [12, 42, 74, 78].”