# An Open-World Extension to Knowledge Graph Completion Models

arXiv: https://arxiv.org/abs/1906.08382 · HTML: https://mlsanity.com/paper/1906.08382/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 16 of 16)

- [OntoZSL: Ontology-enhanced Zero-shot Learning](https://mlsanity.com/paper/2102.07339/) (2021)
- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [Relational Message Passing for Fully Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2210.03994/) (2022)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs](https://mlsanity.com/paper/2302.01859/) (2023)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge](https://mlsanity.com/paper/2301.01172/) (2023)
- [A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training](https://mlsanity.com/paper/2206.05555/) (2022)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2312.04997/) (2023)
- [Social World Knowledge: Modeling and Applications](https://mlsanity.com/paper/2306.16299/) (2023)
- [IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale](https://mlsanity.com/paper/2301.00716/) (2023)
- [A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2310.11917/) (2023)
- [Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors](https://mlsanity.com/paper/2408.06778/) (2024)
- [Improving Continual Learning of Knowledge Graph Embeddings via Informed Initialization](https://mlsanity.com/paper/2511.11118/) (2025)
- [LEC-KG: An LLM-Embedding Collaborative Framework for Domain-Specific Knowledge Graph Construction -- A Case Study on SDGs](https://mlsanity.com/paper/2602.02090/) (2026)

## Abstract (arXiv)

We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual description. Our model combines a regular link prediction model learned from a knowledge graph with word embeddings learned from a textual corpus. After training both independently, we learn a transformation to map the embeddings of an entity's name and description to the graph-based embedding space. In experiments on several datasets including FB20k, DBPedia50k and our new dataset FB15k-237-OWE, we demonstrate competitive results. Particularly, our approach exploits the full knowledge graph structure even when textual descriptions are scarce, does not require a joint training on graph and text, and can be applied to any embedding-based link prediction model, such as TransE, ComplEx and DistMult.