# Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

arXiv: https://arxiv.org/abs/2203.14987 · HTML: https://mlsanity.com/paper/2203.14987/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [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)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [Multi-Channel Graph Neural Network for Entity Alignment](https://mlsanity.com/paper/1908.09898/) (2019)
- [Multi-view Knowledge Graph Embedding for Entity Alignment](https://mlsanity.com/paper/1906.02390/) (2019)
- [Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations](https://mlsanity.com/paper/2011.03880/) (2020)
- [Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment](https://mlsanity.com/paper/1806.06478/) (2018)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Differentiable Open-Ended Commonsense Reasoning](https://mlsanity.com/paper/2010.14439/) (2020)
- [Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer](https://mlsanity.com/paper/2010.03158/) (2020)
- [DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction](https://mlsanity.com/paper/2106.03251/) (2021)
- [Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment](https://mlsanity.com/paper/2104.08804/) (2021)
- [Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network](https://mlsanity.com/paper/2106.06555/) (2021)
- [Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis](https://mlsanity.com/paper/2104.04676/) (2021)
- [Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion](https://mlsanity.com/paper/2108.01387/) (2021)

## Built on by (most cited first, 24 of 24)

- [A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning](https://mlsanity.com/paper/2410.12288/) (2024)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [Generalizing Graph ODE for Learning Complex System Dynamics across Environments](https://mlsanity.com/paper/2307.04287/) (2023)
- [Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs](https://mlsanity.com/paper/2210.08654/) (2022)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [Knowledge Graph Completion with Counterfactual Augmentation](https://mlsanity.com/paper/2302.13083/) (2023)
- [KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation](https://mlsanity.com/paper/2403.14950/) (2024)
- [AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models](https://mlsanity.com/paper/2307.11772/) (2023)
- [Unsupervised Improvement of Factual Knowledge in Language Models](https://mlsanity.com/paper/2304.01597/) (2023)
- [Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs](https://mlsanity.com/paper/2307.01933/) (2023)
- [Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs](https://mlsanity.com/paper/2311.15781/) (2023)
- [Adapters for Enhanced Modeling of Multilingual Knowledge and Text](https://mlsanity.com/paper/2210.13617/) (2022)
- [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2305.18675/) (2023)
- [Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2303.14898/) (2023)
- [Joint Multilingual Knowledge Graph Completion and Alignment](https://mlsanity.com/paper/2210.08922/) (2022)
- [Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering](https://mlsanity.com/paper/2211.08588/) (2022)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [Collective Knowledge Graph Completion with Mutual Knowledge Distillation](https://mlsanity.com/paper/2305.15895/) (2023)
- [FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion](https://mlsanity.com/paper/2312.10645/) (2023)
- [Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production](https://mlsanity.com/paper/2410.18475/) (2024)
- [Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling](https://mlsanity.com/paper/2507.18977/) (2025)
- [Graph Fusion Across Languages using Large Language Models](https://mlsanity.com/paper/2603.21248/) (2026)
- [In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs](https://mlsanity.com/paper/2605.05931/) (2026)
- [Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives](https://mlsanity.com/paper/2607.21660/) (2026)

## Abstract (arXiv)

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages. However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights. Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AG