# Neighborhood Mixture Model for Knowledge Base Completion

arXiv: https://arxiv.org/abs/1606.06461 · HTML: https://mlsanity.com/paper/1606.06461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining](https://mlsanity.com/paper/2108.08983/) (2021)

## Abstract (arXiv)

Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.