Large scale integration of senses for the semantic web
By Jorge Gracia, Mathieu d’Aquin, Eduardo Mena. 2009
45 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Natural Language Processing, Knowledge Graphs & Representation
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
- Simon Lacoste-Julien
- Fabian M. Suchanek
- Zoubin Ghahramani
- Yoshua Bengio
- Gjergji Kasneci
- Yuzhong Qu
- Wei Hu
- Alex Davies
In Computer Networks and Communications
- Fabian M. Suchanek
- Simon Lacoste-Julien
- Alex Davies
- Wei Hu
- Gjergji Kasneci
- Thore Graepel
- Pierre Senellart
- Zoubin Ghahramani
In Information Systems
- Zoubin Ghahramani
- Simon Lacoste-Julien
- Alex Davies
- Wei Hu
- Fabian M. Suchanek
- Gjergji Kasneci
- Thore Graepel
- Pierre Senellart
In Natural Language Processing
- Fabian M. Suchanek
- Pierre Senellart
- Serge Abiteboul
- Simon Lacoste-Julien
- Alex Davies
- Wei Hu
- Gjergji Kasneci
- Thore Graepel
In Knowledge Graphs & Representation
- Fabian M. Suchanek
- Simon Lacoste-Julien
- Alex Davies
- Wei Hu
- Gjergji Kasneci
- Thore Graepel
- Pierre Senellart
- Zoubin Ghahramani
Citations in the corpus
- SIGMa: simple greedy matching for aligning large knowledge bases (2012, background): “On the other hand, the related problem of identifying duplicate entities known as record linkage or duplicate detection in the database field, and co-reference resolution in the natural language processing field, do have scalable solutions using indexing techniques [8, 13], though these do not typic”
- PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, methodology): “In another direction, much research has focused on schema alignment, i.e., on the so-called “T-Box” [3,14,20,21,34].”
- A self-training approach for resolving object coreference on the semantic web (2011, methodology): “For architecture, our proposed framework is similar to the work in [9], which dedicated a large-scale clustering to on-”