Reasoning With Neural Tensor Networks for Knowledge Base Completion

Knowledge bases are an important resource for question answering and other tasks but often suffer from incompleteness and lack of ability to reason over their dis-crete entities and relationships. In this paper we introduce an expressive neu-ral tensor network suitable for reasoning over relationships between two entities. Previous work represented entities as either discrete atomic units or with a single entity vector representation. We show that performance can be improved when en-tities are represented as an average of their constituting word vectors. This allows sharing of statistical strength between, for instance, facts involving the “Sumatran tiger ” and “Bengal tiger. ” Lastly, we demonstrate that all…