# Knowledge Base Completion: Baselines Strike Back

arXiv: https://arxiv.org/abs/1705.10744 · HTML: https://mlsanity.com/paper/1705.10744/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2017.

## Built on by (most cited first, 30 of 39)

- [Accounting for Variance in Machine Learning Benchmarks](https://mlsanity.com/paper/2103.03098/) (2021)
- [Elo Uncovered: Robustness and Best Practices in Language Model Evaluation](https://mlsanity.com/paper/2311.17295/) (2023)
- [Randomness In Neural Network Training: Characterizing The Impact of Tooling](https://mlsanity.com/paper/2106.11872/) (2021)
- [Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations](https://mlsanity.com/paper/2110.02834/) (2021)
- [Role-Aware Modeling for N-ary Relational Knowledge Bases](https://mlsanity.com/paper/2104.09780/) (2021)
- [HousE: Knowledge Graph Embedding with Householder Parameterization](https://mlsanity.com/paper/2202.07919/) (2022)
- [Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods](https://mlsanity.com/paper/2111.03120/) (2021)
- [Knowledge Hypergraph Embedding Meets Relational Algebra](https://mlsanity.com/paper/2102.09557/) (2021)
- [A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms](https://mlsanity.com/paper/2204.09825/) (2022)
- [Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes](https://mlsanity.com/paper/2105.08683/) (2021)
- [CARE: Certifiably Robust Learning with Reasoning via Variational Inference](https://mlsanity.com/paper/2209.05055/) (2022)
- [Sem@K: Is my knowledge graph embedding model semantic-aware?](https://mlsanity.com/paper/2301.05601/) (2023)
- [Learning to Substitute Ingredients in Recipes](https://mlsanity.com/paper/2302.07960/) (2023)
- [Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding](https://mlsanity.com/paper/2106.07250/) (2021)
- [GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding](https://mlsanity.com/paper/2110.06196/) (2021)
- [An Automatic Ontology Generation Framework with An Organizational Perspective](https://mlsanity.com/paper/2201.05910/) (2022)
- [Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2202.09464/) (2022)
- [Stay Positive: Knowledge Graph Embedding Without Negative Sampling](https://mlsanity.com/paper/2201.02661/) (2022)
- [Ranking vs. Classifying: Measuring Knowledge Base Completion Quality](https://mlsanity.com/paper/2102.06145/) (2021)
- [KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models](https://mlsanity.com/paper/2208.11024/) (2022)
- [KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion](https://mlsanity.com/paper/2404.03893/) (2024)
- [What is Learned in Knowledge Graph Embeddings?](https://mlsanity.com/paper/2110.09978/) (2021)
- [Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04605/) (2021)
- [On The Fairness Impacts of Hardware Selection in Machine Learning](https://mlsanity.com/paper/2312.03886/) (2023)
- [A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings](https://mlsanity.com/paper/2412.10092/) (2024)
- [Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph](https://mlsanity.com/paper/2506.21071/) (2025)
- [KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04871/) (2021)
- [SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space](https://mlsanity.com/paper/2204.10245/) (2022)
- [Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality](https://mlsanity.com/paper/2207.00473/) (2022)
- [Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?](https://mlsanity.com/paper/2301.12929/) (2023)

## Abstract (arXiv)

Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline - our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported.