# Investigations on Knowledge Base Embedding for Relation Prediction and Extraction

arXiv: https://arxiv.org/abs/1802.02114 · HTML: https://mlsanity.com/paper/1802.02114/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [Negative Sampling in Knowledge Graph Representation Learning: A Review](https://mlsanity.com/paper/2402.19195/) (2024)
- [Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models](https://mlsanity.com/paper/2411.14858/) (2024)

## Abstract (arXiv)

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.