# SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction

arXiv: https://arxiv.org/abs/2210.04870 · HTML: https://mlsanity.com/paper/2210.04870/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [Quaternion Knowledge Graph Embeddings](https://mlsanity.com/paper/1904.10281/) (2019)
- [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Multi-Hop Knowledge Graph Reasoning with Reward Shaping](https://mlsanity.com/paper/1808.10568/) (2018)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models](https://mlsanity.com/paper/2203.02167/) (2022)
- [PairRE: Knowledge Graph Embeddings via Paired Relation Vectors](https://mlsanity.com/paper/2011.03798/) (2020)
- [On the representation and embedding of knowledge bases beyond binary relations](https://mlsanity.com/paper/1604.08642/) (2016)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Structure Aware Negative Sampling in Knowledge Graphs](https://mlsanity.com/paper/2009.11355/) (2020)
- [Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks](https://mlsanity.com/paper/2007.11192/) (2020)
- [Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion](https://mlsanity.com/paper/2108.01387/) (2021)

## Built on by (most cited first, 12 of 12)

- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [How does Misinformation Affect Large Language Model Behaviors and Preferences?](https://mlsanity.com/paper/2505.21608/) (2025)
- [SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition](https://mlsanity.com/paper/2305.04076/) (2023)
- [BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curation](https://mlsanity.com/paper/2510.13853/) (2025)
- [Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs](https://mlsanity.com/paper/2310.12008/) (2023)
- [Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion](https://mlsanity.com/paper/2412.09094/) (2024)
- [Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2404.00051/) (2024)
- [COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing](https://mlsanity.com/paper/2405.13602/) (2024)
- [Diversified and Adaptive Negative Sampling on Knowledge Graphs](https://mlsanity.com/paper/2410.07592/) (2024)
- [How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method](https://mlsanity.com/paper/2411.00612/) (2024)
- [CHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks](https://mlsanity.com/paper/2501.02760/) (2025)
- [SASA: Semantic-Aware Contrastive Learning Framework with Separated Attention for Triple Classification](https://mlsanity.com/paper/2601.13035/) (2026)

## Abstract (arXiv)

Link prediction is the task of inferring missing links between entities in knowledge graphs. Embedding-based methods have shown effectiveness in addressing this problem by modeling relational patterns in triples. However, the link prediction task often requires contextual information in entity neighborhoods, while most existing embedding-based methods fail to capture it. Additionally, little attention is paid to the diversity of entity representations in different contexts, which often leads to false prediction results. In this situation, we consider that the schema of knowledge graph contains the specific contextual information, and it is beneficial for preserving the consistency of entities across contexts. In this paper, we propose a novel Schema-augmented Multi-level contrastive LEarning framework (SMiLE) to conduct knowledge graph link prediction. Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information. Then we fine-tune our model under the supervision of individual triples to learn subtler representations for link prediction. Extensive experimental results on four knowledge graph datasets with thorough analysis of each component demonstrate the effectiveness of our proposed framework against state-of-the-art baselines. The implementation of SMiLE is available at https://github.com/GKNL/SMiLE.