# Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

arXiv: https://arxiv.org/abs/2009.12765 · HTML: https://mlsanity.com/paper/2009.12765/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [InGram: Inductive Knowledge Graph Embedding via Relation Graphs](https://mlsanity.com/paper/2305.19987/) (2023)
- [Inductive Relation Prediction by BERT](https://mlsanity.com/paper/2103.07102/) (2021)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2312.04997/) (2023)
- [Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2407.02867/) (2024)
- [BED: Bi-Encoder-Decoder Model for Canonical Relation Extraction](https://mlsanity.com/paper/2312.07088/) (2023)
- [Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors](https://mlsanity.com/paper/2408.06778/) (2024)

## Abstract (arXiv)

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to represent these entities efficiently. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method needs no additional learning. Experimental results show that our method outperforms the state-of-the-art methods with higher efficiency on two KGC tasks with OOKG entities.