# Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer

arXiv: https://arxiv.org/abs/2010.03158 · HTML: https://mlsanity.com/paper/2010.03158/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning](https://mlsanity.com/paper/2410.12288/) (2024)
- [Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment](https://mlsanity.com/paper/2203.14987/) (2022)
- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2210.08821/) (2022)
- [Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment](https://mlsanity.com/paper/2104.08804/) (2021)
- [Prix-LM: Pretraining for Multilingual Knowledge Base Construction](https://mlsanity.com/paper/2110.08443/) (2021)
- [Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs](https://mlsanity.com/paper/2207.11436/) (2022)
- [mOKB6: A Multilingual Open Knowledge Base Completion Benchmark](https://mlsanity.com/paper/2211.06959/) (2022)
- [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)
- [Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs](https://mlsanity.com/paper/2306.02679/) (2023)
- [Dangling-Aware Entity Alignment with Mixed High-Order Proximities](https://mlsanity.com/paper/2205.02406/) (2022)
- [μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications](https://mlsanity.com/paper/2207.11442/) (2022)
- [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)
- [Knowledge Graph Embedding with 3D Compound Geometric Transformations](https://mlsanity.com/paper/2304.00378/) (2023)
- [Pre-training Transformers for Knowledge Graph Completion](https://mlsanity.com/paper/2303.15682/) (2023)
- [Collective Knowledge Graph Completion with Mutual Knowledge Distillation](https://mlsanity.com/paper/2305.15895/) (2023)
- [Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production](https://mlsanity.com/paper/2410.18475/) (2024)
- [KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs](https://mlsanity.com/paper/2501.03560/) (2025)
- [GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds](https://mlsanity.com/paper/2505.02972/) (2025)
- [In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs](https://mlsanity.com/paper/2605.05931/) (2026)
- [Cross-Source Reasoning-based Correction for Author Name Disambiguation](https://mlsanity.com/paper/2606.08617/) (2026)
- [Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion](https://mlsanity.com/paper/2607.03154/) (2026)

## Abstract (arXiv)

Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings. While existing KG embedding approaches mainly learn and predict facts within a single KG, a more plausible solution would benefit from the knowledge in multiple language-specific KGs, considering that different KGs have their own strengths and limitations on data quality and coverage. This is quite challenging, since the transfer of knowledge among multiple independently maintained KGs is often hindered by the insufficiency of alignment information and the inconsistency of described facts. In this paper, we propose KEnS, a novel framework for embedding learning and ensemble knowledge transfer across a number of language-specific KGs. KEnS embeds all KGs in a shared embedding space, where the association of entities is captured based on self-learning. Then, KEnS performs ensemble inference to combine prediction results from embeddings of multiple language-specific KGs, for which multiple ensemble techniques are investigated. Experiments on five real-world language-specific KGs show that KEnS consistently improves state-of-the-art methods on KG completion, via effectively identifying and leveraging complementary knowledge.