# SEEK: Segmented Embedding of Knowledge Graphs

arXiv: https://arxiv.org/abs/2005.00856 · HTML: https://mlsanity.com/paper/2005.00856/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 11 of 11)

- [Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis](https://mlsanity.com/paper/2104.04676/) (2021)
- [TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors](https://mlsanity.com/paper/2209.08271/) (2022)
- [Knowledge Graph Embedding: An Overview](https://mlsanity.com/paper/2309.12501/) (2023)
- [TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction](https://mlsanity.com/paper/2204.13221/) (2022)
- [MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy](https://mlsanity.com/paper/2110.15087/) (2021)
- [SHGNN: Structure-Aware Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2112.06244/) (2021)
- [Knowledge Graph Embedding with 3D Compound Geometric Transformations](https://mlsanity.com/paper/2304.00378/) (2023)
- [MQuinE: a cure for "Z-paradox" in knowledge graph embedding models](https://mlsanity.com/paper/2402.03583/) (2024)
- [ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2312.07589/) (2023)
- [KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04871/) (2021)
- [Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion](https://mlsanity.com/paper/2307.05627/) (2023)

## Abstract (arXiv)

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at urlhttps://github.com/Wentao-Xu/SEEK.