Sainyam Galhotra, Udayan Khurana, et al.
ICDM 2019
Knowledge Graphs (KGs) play a key role in many artificial intelligence applications. Large KGs are often constructed through a noisy automatic knowledge extraction process. Noise detection is, therefore, an important task for having high-quality KGs. We argue that the current noise detection approaches only focus on a specific type of noise (i.e., fact checking) whereas knowledge extraction methods result in more than one type of noise. To this end, we propose a classification of noise found in automatically-constructed KGs, and an approach for noise detection focused on specific types of noise.
Sainyam Galhotra, Udayan Khurana, et al.
ICDM 2019
Vasilis Efthymiou, Oktie Hassanzadeh, et al.
OM 2016
Shirin Sohrabi, Michael Katz, et al.
AI Communications
Oktie Hassanzadeh
ISWC-Posters-Demos-Industry 2021