Reasoning With Neural Tensor Networks for Knowledge Base Completion (2013)
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**Predicting the likely truth of additional facts based on existing facts in the knowledge base.** > we introduce an expressive neural tensor network suitable for reasoning over relationships between two entities. Most similar work: [Bordes et al.](http://127.0.0.1:8080/semanlink/doc/2019/08/learning_structured_embeddings_) (2011) Contributions: 1. new neural tensor network (NTN) suitable for reasoning over relationships between two entities.. Generalizes several previous neural network models and provides a more powerful way to model relational information than a standard neural network layer. 2. a new way to represent entities in knowledge bases, as the average of their constituting word vectorss, allowing the sharing of statistical strength between the words describing each entity (e.g., Bank of China and China). 3. incorporation of word vectors which are trained on large unlabeled text > We learn to modify word representations via grounding in world knowledge. This essentially allows us to analyze word embeddings and query them for specific relations. Furthermore, the resulting vectors could be used in other tasks such as named entity recognition or relation classification in natural language
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