QOM - Quick Ontology Mapping
By Marc Ehrig, Steffen Staab. 2004
535 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Natural Language Processing
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
- Pavel Shvaiko
- Jérôme Euzenat
- Simon Lacoste-Julien
- Jie Tang
- Yuzhong Qu
- Wei Hu
- Zoubin Ghahramani
- Francisco Javier Rodríguez-Martínez
In Information Systems
- Pavel Shvaiko
- Jérôme Euzenat
- Wei Hu
- Yuzhong Qu
- Francisco Javier Rodríguez-Martínez
- Lorena Otero-Cerdeira
- Alma María Gómez-Rodríguez
- Zoubin Ghahramani
In Management Science and Operations Research
- Jérôme Euzenat
- Pavel Shvaiko
- Zoubin Ghahramani
- Simon Lacoste-Julien
- Wei Hu
- Francisco Javier Rodríguez-Martínez
- Lorena Otero-Cerdeira
- Alex Davies
In Natural Language Processing
- Jie Tang
- Simon Lacoste-Julien
- Francisco Javier Rodríguez-Martínez
- Lorena Otero-Cerdeira
- Alex Davies
- Wei Hu
- Gjergji Kasneci
- Thore Graepel
Citations in the corpus
- SIGMa: simple greedy matching for aligning large knowledge bases (2012, background): “[11] M. Ehrig and S. Staab.”
- A String Metric for Ontology Alignment (2005, unknown): “Many platforms that use such a methodology to compute similarities between ontologies exist in literature; some examples are, Anchor-PROMPT [3], QOM [4], Cupid [5] and many more.”
- GMO: A Graph Matching for Ontologies (2005, background): “QOM-Quick Ontology Mapping.”
- Semantic Explanations of Predictions (2018, background): “The concept mapping is achieved following (Ehrig and Staab 2004) where both syntactic and semantic similarities (distance among similar concepts) are considered.”
- Ontology Matching: State of the Art and Future Challenges (2013, methodology): “A straightforward approach here is to reduce the number of pair-wise comparisons in favor of a ( n incomplete) top-down strategy as implemented in QOM [99], or to avoid using computationally expensive matching methods, such as in RiMOM [47] by suppressing the structure based strategies and by applyi”
- A Survey of Schema-Based Matching Approaches (2005, unknown): “Analysis of state of the art systems discussed has shown, that most of them exploit only syntactic and external techniques, and only one uses semantic techniques.”
- Ontology Matching (2007, methodology): “QOM (Quick Ontology Mapping) (Ehrig and Staab 2004) is a variation of the NOM system dedicated to improve the efficiency of the system.”
- Ontology matching: A literature review (2015, methodology): “This system was developed in the first years of the interval considered in this literature review in 2 papers (Ehrig & Staab, 2004; Ehrig & Sure, 2004).”
- RiMOM: A Dynamic Multistrategy Ontology Alignment Framework (2009, background): “QOM addresses the efficiency problem of alignment [14].”