# LinkNBed: Multi-Graph Representation Learning with Entity Linkage

arXiv: https://arxiv.org/abs/1807.08447 · HTML: https://mlsanity.com/paper/1807.08447/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications](https://mlsanity.com/paper/2402.12608/) (2024)
- [Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs](https://mlsanity.com/paper/2306.02679/) (2023)
- [Business Entity Matching with Siamese Graph Convolutional Networks](https://mlsanity.com/paper/2105.03701/) (2021)
- [μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications](https://mlsanity.com/paper/2207.11442/) (2022)
- [What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings](https://mlsanity.com/paper/2306.02622/) (2023)
- [A Self-supervised Method for Entity Alignment](https://mlsanity.com/paper/2106.09395/) (2021)
- [Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links](https://mlsanity.com/paper/2309.05201/) (2023)
- [Technological Approaches to Detecting Online Disinformation and Manipulation](https://mlsanity.com/paper/2108.11669/) (2021)
- [LTCR: Long-Text Chinese Rumor Detection Dataset](https://mlsanity.com/paper/2306.07201/) (2023)

## Abstract (arXiv)

Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs and eventually construct a unified graph is a crucial next step for the success of knowledge-based inference for many downstream applications. To this end, we propose LinkNBed, a deep relational learning framework that learns entity and relationship representations across multiple graphs. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure. Experiments on link prediction and entity linkage demonstrate substantial improvements over the state-of-the-art relational learning approaches.