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Found 9 papers, 6 papers with code
Efficient Entity Candidate Generation for Low-Resource Languages
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.
A Critical Re-evaluation of Neural Methods for Entity Alignment
Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks.
Low-Rank Subspaces for Unsupervised Entity Linking
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.
Recursive input and state estimation: A general framework for learning from time series with missing data
Time series with missing data are signals encountered in important settings for machine learning.
Learning Numerical Attributes in Knowledge Bases
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.
Knowledge Graph Completion to Predict Polypharmacy Side Effects
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.
Learning Sequence Encoders for Temporal Knowledge Graph Completion
In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances.
