Search Results for author: Alberto García-Durán

Found 9 papers, 6 papers with code

Efficient Entity Candidate Generation for Low-Resource Languages

1 code implementation LREC 2022 Alberto García-Durán, Akhil Arora, Robert West

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.

Cross-Lingual Entity Linking Entity Linking +1

A Critical Re-evaluation of Neural Methods for Entity Alignment

1 code implementation PVLDB 2022 Manuel Leone, Stefano Huber, Akhil Arora, Alberto García-Durán, Robert West

Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks.

Entity Alignment Entity Resolution +1

Low-Rank Subspaces for Unsupervised Entity Linking

1 code implementation EMNLP 2021 Akhil Arora, Alberto García-Durán, Robert West

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.

Entity Linking

Learning Numerical Attributes in Knowledge Bases

no code implementations AKBC 2019 Bhushan Kotnis, Alberto García-Durán

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.

Link Prediction

Knowledge Graph Completion to Predict Polypharmacy Side Effects

no code implementations22 Oct 2018 Brandon Malone, Alberto García-Durán, Mathias Niepert

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.

Knowledge Graph Completion

Learning Sequence Encoders for Temporal Knowledge Graph Completion

3 code implementations EMNLP 2018 Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert

In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.

Link Prediction Temporal Knowledge Graph Completion

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