# Temporal Knowledge Graph Completion using Box Embeddings

arXiv: https://arxiv.org/abs/2109.08970 · HTML: https://mlsanity.com/paper/2109.08970/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [TuckER: Tensor Factorization for Knowledge Graph Completion](https://mlsanity.com/paper/1901.09590/) (2019)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1809.03202/) (2018)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [BoxE: A Box Embedding Model for Knowledge Base Completion](https://mlsanity.com/paper/2007.06267/) (2020)
- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1907.03143/) (2019)
- [TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2010.03526/) (2020)
- [Question Answering with Subgraph Embeddings](https://mlsanity.com/paper/1406.3676/) (2014)
- [TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation](https://mlsanity.com/paper/2010.01029/) (2020)
- [ChronoR: Rotation Based Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2103.10379/) (2021)

## Built on by (most cited first, 30 of 34)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [A Survey on Temporal Knowledge Graph: Representation Learning and Applications](https://mlsanity.com/paper/2403.04782/) (2024)
- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)
- [Faithful Temporal Question Answering over Heterogeneous Sources](https://mlsanity.com/paper/2402.15400/) (2024)
- [Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2307.10219/) (2023)
- [HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces](https://mlsanity.com/paper/2312.13680/) (2023)
- [IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2403.19881/) (2024)
- [A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2112.07791/) (2021)
- [Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph](https://mlsanity.com/paper/2405.00352/) (2024)
- [Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings](https://mlsanity.com/paper/2203.12949/) (2022)
- [NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs](https://mlsanity.com/paper/2305.11301/) (2023)
- [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2305.18675/) (2023)
- [Reasoning over Multi-view Knowledge Graphs](https://mlsanity.com/paper/2209.13702/) (2022)
- [Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2408.06603/) (2024)
- [Neurosymbolic Methods for Dynamic Knowledge Graphs](https://mlsanity.com/paper/2409.04572/) (2024)
- [Search to Pass Messages for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2210.16740/) (2022)
- [Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning](https://mlsanity.com/paper/2304.00613/) (2023)
- [Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2405.00358/) (2024)
- [Insert or Attach: Taxonomy Completion via Box Embedding](https://mlsanity.com/paper/2305.11004/) (2023)
- [Temporal Smoothness Regularisers for Neural Link Predictors](https://mlsanity.com/paper/2309.09045/) (2023)
- [DURENDAL: Graph deep learning framework for temporal heterogeneous networks](https://mlsanity.com/paper/2310.00336/) (2023)
- [SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding](https://mlsanity.com/paper/2402.12132/) (2024)
- [Selective Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2404.01695/) (2024)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Towards Knowledge-Centric Process Mining](https://mlsanity.com/paper/2301.10927/) (2023)
- [Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space](https://mlsanity.com/paper/2403.02355/) (2024)
- [Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2404.09155/) (2024)
- [Learning Granularity Representation for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2408.15293/) (2024)

## Abstract (arXiv)

Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp. Current approaches for TKGC primarily build on existing embedding models which are developed for (static) knowledge graph completion, and extend these models to incorporate time, where the idea is to learn latent representations for entities, relations, and timestamps and then use the learned representations to predict missing facts at various time steps. In this paper, we propose BoxTE, a box embedding model for TKGC, building on the static knowledge graph embedding model BoxE. We show that BoxTE is fully expressive, and possesses strong inductive capacity in the temporal setting. We then empirically evaluate our model and show that it achieves state-of-the-art results on several TKGC benchmarks.