# Collective Knowledge Graph Completion with Mutual Knowledge Distillation

arXiv: https://arxiv.org/abs/2305.15895 · HTML: https://mlsanity.com/paper/2305.15895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Graph neural network distillation, one of 12 directions in Knowledge distillation.
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 25 May 2023.

## Placed in

- Knowledge distillation / [Graph neural network distillation](https://mlsanity.com/distill/graph-neural-network-distillation/)

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (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)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer](https://mlsanity.com/paper/2010.03158/) (2020)
- [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)
- [Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment](https://mlsanity.com/paper/2104.08804/) (2021)
- [An Adversarial Transfer Network for Knowledge Representation Learning](https://mlsanity.com/paper/2104.14757/) (2021)

## Built on by (most cited first, 1 of 1)

- [FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion](https://mlsanity.com/paper/2312.10645/) (2023)

## Abstract (arXiv)

Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years. However, the predictive power of KGC methods is often limited by the completeness of the existing knowledge graphs from different sources and languages. In monolingual and multilingual settings, KGs are potentially complementary to each other. In this paper, we study the problem of multi-KG completion, where we focus on maximizing the collective knowledge from different KGs to alleviate the incompleteness of individual KGs. Specifically, we propose a novel method called CKGC-CKD that uses relation-aware graph convolutional network encoder models on both individual KGs and a large fused KG in which seed alignments between KGs are regarded as edges for message propagation. An additional mutual knowledge distillation mechanism is also employed to maximize the knowledge transfer between the models of "global" fused KG and the "local" individual KGs. Experimental results on multilingual datasets have shown that our method outperforms all state-of-the-art models in the KGC task.