LINDA: distributed web-of-data-scale entity matching
By Christoph Böhm, Gerard de Melo, Felix Naumann, Gerhard Weikum. 2012
99 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, 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
- Roberto Navigli
- Alessandro Raganato
- Simon Lacoste-Julien
- Zoubin Ghahramani
- Andrea Moro
- Mohammad Taher Pilehvar
- José Camacho-Collados
- Yoshua Bengio
In Computer Networks and Communications
- Simon Lacoste-Julien
- Alex Davies
- Andrea Moro
- Gjergji Kasneci
- Thore Graepel
- Roberto Navigli
- Zoubin Ghahramani
- Konstantina Palla
In Management Science and Operations Research
- Zoubin Ghahramani
- Simon Lacoste-Julien
- Alex Davies
- Gjergji Kasneci
- Thore Graepel
- Roberto Navigli
- Konstantina Palla
- Jiaxin Shi
In Natural Language Processing
- Alessandro Raganato
- Roberto Navigli
- Andrea Moro
- Simon Lacoste-Julien
- Alex Davies
- Gjergji Kasneci
- Thore Graepel
- Zoubin Ghahramani
Citations in the corpus
- SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “Böhm et al. have recently proposed a similar algorithm to SiGMA called LINDA [5], independently to our prior technical report [17].”
- Entity Linking meets Word Sense Disambiguation: a Unified Approach (2014, background): “Recently, the explosion of on-line social networking services, such as Twitter and Facebook, have contributed to the development of new methods for the efficient disambiguation of short texts (Ferrag-ina and Scaiella, 2010; Hoffart et al., 2012; B¨ohm et al., 2012).”