b337e84de8752b27eda3a12363109e80-Reviews.html

This paper proposes a new model for link prediction in knowledge bases. By learning a non-linear scoring function based on tensor and matrix operators for representing relations and word embeddings for representing entities, this approach can outperform previous work on data from two knowledge bases: Wordnet and Freebase. Clarity: The paper is very clear and nicely written. I would only reduce the use of the term "reasoning", which is not obviously justified in this context and can be misleading. It seems that link prediction is more performed using (well-trained) similarity measures than by relying on (formal) reasoning. Originality: While building on previous work (cited), the model architecture is original and the use of word embeddings (learned on text corpora) is also brand new.