# A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

arXiv: https://arxiv.org/abs/2308.07889 · HTML: https://mlsanity.com/paper/2308.07889/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [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)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Harnessing Deep Neural Networks with Logic Rules](https://mlsanity.com/paper/1603.06318/) (2016)
- [Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction](https://mlsanity.com/paper/1911.09419/) (2019)
- [Probabilistic Logic Neural Networks for Reasoning](https://mlsanity.com/paper/1906.08495/) (2019)
- [PairRE: Knowledge Graph Embeddings via Paired Relation Vectors](https://mlsanity.com/paper/2011.03798/) (2020)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [Knowledge Graph Embedding with Iterative Guidance from Soft Rules](https://mlsanity.com/paper/1711.11231/) (2017)
- [Leveraging Knowledge Bases in LSTMs for Improving Machine Reading](https://mlsanity.com/paper/1902.09091/) (2019)
- [Open Question Answering with Weakly Supervised Embedding Models](https://mlsanity.com/paper/1404.4326/) (2014)
- [Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective](https://mlsanity.com/paper/2202.07412/) (2022)
- [Rule-Guided Compositional Representation Learning on Knowledge Graphs](https://mlsanity.com/paper/1911.08935/) (2019)
- [Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning](https://mlsanity.com/paper/2206.10140/) (2022)
- [Ruleformer: Context-aware Differentiable Rule Mining over Knowledge Graph](https://mlsanity.com/paper/2209.05815/) (2022)
- [Stay Positive: Knowledge Graph Embedding Without Negative Sampling](https://mlsanity.com/paper/2201.02661/) (2022)
- [Instance-based Learning for Knowledge Base Completion](https://mlsanity.com/paper/2211.06807/) (2022)
- [A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs](https://mlsanity.com/paper/2009.10800/) (2020)
- [Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding](https://mlsanity.com/paper/2110.00720/) (2021)

## Built on by (most cited first, 3 of 3)

- [PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips](https://mlsanity.com/paper/2309.03685/) (2023)
- [The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models](https://mlsanity.com/paper/2409.04103/) (2024)
- [Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations](https://mlsanity.com/paper/2505.12369/) (2025)

## Abstract (arXiv)

Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns are an important factor in the performance of KGE models. Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern. To address this challenge, we evaluate the performance of 7 KGE models over 4 common relational patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency, and part-to-whole three aspects and get some counterintuitive conclusions. Finally, we introduce a training-free method Score-based Patterns Adaptation (SPA) to enhance KGE models' performance over various relational patterns. This approach is simple yet effective and can be applied to KGE models without additional training. Our experimental results demonstrate that our method generally enhances performance over specific relational patterns. Our source code is available from GitHub at https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns.