# Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

arXiv: https://arxiv.org/abs/1903.08948 · HTML: https://mlsanity.com/paper/1903.08948/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 30 of 63)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Simple and Efficient Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2207.02547/) (2022)
- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14170/) (2021)
- [Ontology-enhanced Prompt-tuning for Few-shot Learning](https://mlsanity.com/paper/2201.11332/) (2022)
- [OntoED: Low-resource Event Detection with Ontology Embedding](https://mlsanity.com/paper/2105.10922/) (2021)
- [TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs](https://mlsanity.com/paper/2402.12309/) (2024)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective](https://mlsanity.com/paper/2202.07412/) (2022)
- [Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling](https://mlsanity.com/paper/2209.07084/) (2022)
- [Neural-Symbolic Entangled Framework for Complex Query Answering](https://mlsanity.com/paper/2209.08779/) (2022)
- [Data Scarcity in Recommendation Systems: A Survey](https://mlsanity.com/paper/2312.10073/) (2023)
- [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://mlsanity.com/paper/2303.03922/) (2023)
- [Graph Collaborative Reasoning](https://mlsanity.com/paper/2112.13705/) (2021)
- [Edge: Enriching Knowledge Graph Embeddings with External Text](https://mlsanity.com/paper/2104.04909/) (2021)
- [Billion-scale Pre-trained E-commerce Product Knowledge Graph Model](https://mlsanity.com/paper/2105.00388/) (2021)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks](https://mlsanity.com/paper/2305.19979/) (2023)
- [NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs](https://mlsanity.com/paper/2202.12571/) (2022)
- [Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding](https://mlsanity.com/paper/2302.01849/) (2023)
- [OntoTune: Ontology-Driven Self-training for Aligning Large Language Models](https://mlsanity.com/paper/2502.05478/) (2025)
- [Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding](https://mlsanity.com/paper/2304.11618/) (2023)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference](https://mlsanity.com/paper/2112.01040/) (2021)
- [Knowledge Graphs Querying](https://mlsanity.com/paper/2305.14485/) (2023)
- [Conditional Logical Message Passing Transformer for Complex Query Answering](https://mlsanity.com/paper/2402.12954/) (2024)
- [Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection](https://mlsanity.com/paper/2310.12011/) (2023)
- [Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules](https://mlsanity.com/paper/2112.08589/) (2021)
- [A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning](https://mlsanity.com/paper/2308.07889/) (2023)
- [Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking](https://mlsanity.com/paper/2310.07613/) (2023)

## Abstract (arXiv)

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.