# Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment

arXiv: https://arxiv.org/abs/2104.08804 · HTML: https://mlsanity.com/paper/2104.08804/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [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)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Learning Latent Permutations with Gumbel-Sinkhorn Networks](https://mlsanity.com/paper/1802.08665/) (2018)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding](https://mlsanity.com/paper/1708.05045/) (2017)
- [Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation](https://mlsanity.com/paper/1911.08936/) (2019)
- [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)
- [Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols](https://mlsanity.com/paper/2005.05035/) (2020)
- [Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer](https://mlsanity.com/paper/2010.03158/) (2020)
- [Knowledge Base Completion: Baseline strikes back (Again)](https://mlsanity.com/paper/2005.00804/) (2020)
- [Learning Functions over Sets via Permutation Adversarial Networks](https://mlsanity.com/paper/1907.05638/) (2019)

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

- [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)
- [Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs](https://mlsanity.com/paper/2207.11436/) (2022)
- [PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2110.07415/) (2021)
- [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)
- [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)
- [EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs](https://mlsanity.com/paper/2211.02817/) (2022)
- [Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion](https://mlsanity.com/paper/2607.03154/) (2026)

## Abstract (arXiv)

Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph. Almost all of existing KGC research is applicable to only one KG at a time, and in one language only. However, different language speakers may maintain separate KGs in their language and no individual KG is expected to be complete. Moreover, common entities or relations in these KGs have different surface forms and IDs, leading to ID proliferation. Entity alignment (EA) and relation alignment (RA) tasks resolve this by recognizing pairs of entity (relation) IDs in different KGs that represent the same entity (relation). This can further help prediction of missing facts, since knowledge from one KG is likely to benefit completion of another. High confidence predictions may also add valuable information for the alignment tasks. In response, we study the novel task of jointly training multilingual KGC, relation alignment and entity alignment models. We present ALIGNKGC, which uses some seed alignments to jointly optimize all three of KGC, EA and RA losses. A key component of ALIGNKGC is an embedding based soft notion of asymmetric overlap defined on the (subject, object) set signatures of relations this aids in better predicting relations that are equivalent to or implied by other relations. Extensive experiments with DBPedia in five languages establish the benefits of joint training for all tasks, achieving 10-32 MRR improvements of ALIGNKGC over a strong state-of-the-art single-KGC system completion model over each monolingual KG . Further, ALIGNKGC achieves reasonable gains in EA and RA tasks over a vanilla completion model over a KG that combines all facts without alignment, underscoring the value of joint training for these tasks.