We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches.
Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks.
We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base.
Time series with missing data are signals encountered in important settings for machine learning.
It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction.
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests.
In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances.