# Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network

arXiv: https://arxiv.org/abs/1808.03752 · HTML: https://mlsanity.com/paper/1808.03752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path](https://mlsanity.com/paper/2106.09923/) (2021)
- [V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs](https://mlsanity.com/paper/2208.01735/) (2022)

## Abstract (arXiv)

Knowledge Graph Embedding (KGE) aims to represent entities and relations of knowledge graph in a low-dimensional continuous vector space. Recent works focus on incorporating structural knowledge with additional information, such as entity descriptions, relation paths and so on. However, common used additional information usually contains plenty of noise, which makes it hard to learn valuable representation. In this paper, we propose a new kind of additional information, called entity neighbors, which contain both semantic and topological features about given entity. We then develop a deep memory network model to encode information from neighbors. Employing a gating mechanism, representations of structure and neighbors are integrated into a joint representation. The experimental results show that our model outperforms existing KGE methods utilizing entity descriptions and achieves state-of-the-art metrics on 4 datasets.