# A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

arXiv: https://arxiv.org/abs/1808.04122 · HTML: https://mlsanity.com/paper/1808.04122/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

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

- [A Survey on Temporal Knowledge Graph: Representation Learning and Applications](https://mlsanity.com/paper/2403.04782/) (2024)
- [Marius: Learning Massive Graph Embeddings on a Single Machine](https://mlsanity.com/paper/2101.08358/) (2021)
- [Rethinking Graph Convolutional Networks in Knowledge Graph Completion](https://mlsanity.com/paper/2202.05679/) (2022)
- [Multi-task Pre-training Language Model for Semantic Network Completion](https://mlsanity.com/paper/2201.04843/) (2022)
- [KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction](https://mlsanity.com/paper/2211.02744/) (2022)
- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)
- [Trans4E: Link Prediction on Scholarly Knowledge Graphs](https://mlsanity.com/paper/2107.03297/) (2021)
- [QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion](https://mlsanity.com/paper/2105.09002/) (2021)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (2022)
- [r-GAT: Relational Graph Attention Network for Multi-Relational Graphs](https://mlsanity.com/paper/2109.05922/) (2021)
- [Negative Sampling in Knowledge Graph Representation Learning: A Review](https://mlsanity.com/paper/2402.19195/) (2024)
- [IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2403.19881/) (2024)
- [Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings](https://mlsanity.com/paper/2203.12949/) (2022)
- [Large Language Model Enhanced Knowledge Representation Learning: A Survey](https://mlsanity.com/paper/2407.00936/) (2024)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [EchoEA: Echo Information between Entities and Relations for Entity Alignment](https://mlsanity.com/paper/2107.03054/) (2021)
- [A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution](https://mlsanity.com/paper/2108.13024/) (2021)
- [StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph](https://mlsanity.com/paper/2205.14209/) (2022)
- [HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation](https://mlsanity.com/paper/2404.09848/) (2024)
- [Leveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2404.17590/) (2024)
- [Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties](https://mlsanity.com/paper/2410.14733/) (2024)
- [From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding](https://mlsanity.com/paper/2303.12816/) (2023)
- [Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing](https://mlsanity.com/paper/2404.00589/) (2024)
- [Is Large Language Model Good at Triple Set Prediction? An Empirical Study](https://mlsanity.com/paper/2412.18443/) (2024)
- [A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking](https://mlsanity.com/paper/2104.13046/) (2021)
- [Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding](https://mlsanity.com/paper/2210.11711/) (2022)
- [Joint embedding in Hierarchical distance and semantic representation learning for link prediction](https://mlsanity.com/paper/2303.15655/) (2023)
- [Hierarchical Blockmodelling for Knowledge Graphs](https://mlsanity.com/paper/2408.15649/) (2024)
- [Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2507.03947/) (2025)

## Abstract (arXiv)

In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.