# Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information

arXiv: https://arxiv.org/abs/2211.08169 · HTML: https://mlsanity.com/paper/2211.08169/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 51 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 30 of 33)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach](https://mlsanity.com/paper/1706.05674/) (2017)
- [One-Shot Relational Learning for Knowledge Graphs](https://mlsanity.com/paper/1808.09040/) (2018)
- [Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1909.01515/) (2019)
- [TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2109.04101/) (2021)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [Open-World Knowledge Graph Completion](https://mlsanity.com/paper/1711.03438/) (2017)
- [TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2010.03526/) (2020)
- [Temporal Knowledge Graph Forecasting with Neural ODE](https://mlsanity.com/paper/2101.05151/) (2021)
- [Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/1811.01399/) (2018)
- [TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation](https://mlsanity.com/paper/2010.01029/) (2020)
- [Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction](https://mlsanity.com/paper/2006.06648/) (2020)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Adaptive Attentional Network for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2010.09638/) (2020)
- [ChronoR: Rotation Based Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2103.10379/) (2021)
- [DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2011.03984/) (2020)
- [Embedding Models for Episodic Knowledge Graphs](https://mlsanity.com/paper/1807.00228/) (2018)
- [Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion](https://mlsanity.com/paper/1909.11359/) (2019)
- [BiQUE: Biquaternionic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/2109.14401/) (2021)
- [One-shot Learning for Temporal Knowledge Graphs](https://mlsanity.com/paper/2010.12144/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models](https://mlsanity.com/paper/2310.07793/) (2023)
- [Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs](https://mlsanity.com/paper/2302.01859/) (2023)
- [zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2311.10112/) (2023)
- [Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2307.10219/) (2023)
- [TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion](https://mlsanity.com/paper/2504.03720/) (2025)
- [Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning](https://mlsanity.com/paper/2304.00613/) (2023)
- [Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2404.00051/) (2024)

## Abstract (arXiv)

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities. Compared to KGs, TKGs require temporal reasoning techniques for modeling, which naturally increases the difficulty in dealing with novel, yet unseen entities. In this work, we focus on the inductive learning of unseen entities' representations on TKGs. We propose a few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the missing entities from the links concerning unseen entities by employing a meta-learning framework and utilizing the meta-information provided by only few edges associated with each unseen entity. We construct three new datasets for TKG few-shot OOG link prediction, and we propose a model that mines the concept-aware information among entities. Experimental results show that our model achieves superior performance on all three datasets and our concept-aware modeling component demonstrates a strong effect.