# Trans4E: Link Prediction on Scholarly Knowledge Graphs arXiv: https://arxiv.org/abs/2107.03297 ยท HTML: https://mlsanity.com/paper/2107.03297/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 65 references that are in the map. - First posted on arXiv 3 Jul 2021. ## Builds on (most cited first, 27 of 27) - [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019) - [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [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) - [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020) - [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019) - [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015) - [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017) - [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018) - [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019) - [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018) - [Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems](https://mlsanity.com/paper/1905.04413/) (2019) - [Graphonomy: Universal Human Parsing via Graph Transfer Learning](https://mlsanity.com/paper/1904.04536/) (2019) - [Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1909.01515/) (2019) - [A Re-evaluation of Knowledge Graph Completion Methods](https://mlsanity.com/paper/1911.03903/) (2019) - [Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/1911.11298/) (2019) - [Meta-learning for Few-shot Natural Language Processing: A Survey](https://mlsanity.com/paper/2007.09604/) (2020) - [A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization](https://mlsanity.com/paper/1808.04122/) (2018) - [vGraph: A Generative Model for Joint Community Detection and Node Representation Learning](https://mlsanity.com/paper/1906.07159/) (2019) - [A Survey on Graph Neural Networks for Knowledge Graph Completion](https://mlsanity.com/paper/2007.12374/) (2020) - [Meta-Graph: Few Shot Link Prediction via Meta Learning](https://mlsanity.com/paper/1912.09867/) (2019) - [Learning from Few Samples: A Survey](https://mlsanity.com/paper/2007.15484/) (2020) - [Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets](https://mlsanity.com/paper/1611.04687/) (2016) - [OpenCitations, an infrastructure organization for open scholarship](https://mlsanity.com/paper/1906.11964/) (2019) - [Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space](https://mlsanity.com/paper/1909.08191/) (2019) - [A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development](https://mlsanity.com/paper/1909.00672/) (2019) ## Built on by (most cited first, 13 of 13) - [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023) - [EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2111.00276/) (2021) - [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022) - [SCGG: A Deep Structure-Conditioned Graph Generative Model](https://mlsanity.com/paper/2209.09681/) (2022) - [ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs](https://mlsanity.com/paper/2503.06479/) (2025) - [IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling](https://mlsanity.com/paper/2301.02445/) (2023) - [Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation](https://mlsanity.com/paper/2412.04076/) (2024) - [GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes](https://mlsanity.com/paper/2205.01331/) (2022) - [Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction](https://mlsanity.com/paper/2211.12266/) (2022) - [Logic Diffusion for Knowledge Graph Reasoning](https://mlsanity.com/paper/2306.03515/) (2023) - [Tripl`etoile: Extraction of Knowledge from Microblogging Text](https://mlsanity.com/paper/2408.14908/) (2024) - [MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion](https://mlsanity.com/paper/2409.17536/) (2024) - [SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2503.06475/) (2025) ## Abstract (arXiv) The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based services. In the scholarly domain, KGs describing research publications typically lack important information, hindering our ability to analyse and predict research dynamics. In recent years, link prediction approaches based on Knowledge Graph Embedding models became the first aid for this issue. In this work, we present Trans4E, a novel embedding model that is particularly fit for KGs which include N to M relations with NggM. This is typical for KGs that categorize a large number of entities (e.g., research articles, patents, persons) according to a relatively small set of categories. Trans4E was applied on two large-scale knowledge graphs, the Academia/Industry DynAmics (AIDA) and Microsoft Academic Graph (MAG), for completing the information about Fields of Study (e.g., 'neural networks', 'machine learning', 'artificial intelligence'), and affiliation types (e.g., 'education', 'company', 'government'), improving the scope and accuracy of the resulting data. We evaluated our approach against alternative solutions on AIDA, MAG, and four other benchmarks (FB15k, FB15k-237, WN18, and WN18RR). Trans4E outperforms the other models when using low embedding dimensions and obtains competitive results in high dimensions.