# ReliK: A Reliability Measure for Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2404.16572 · HTML: https://mlsanity.com/paper/2404.16572/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion](https://mlsanity.com/paper/2504.08970/) (2025)
- [VL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings](https://mlsanity.com/paper/2603.02435/) (2026)
- [When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking](https://mlsanity.com/paper/2606.10287/) (2026)

## Abstract (arXiv)

Can we assess a priori how well a knowledge graph embedding will perform on a specific downstream task and in a specific part of the knowledge graph? Knowledge graph embeddings (KGEs) represent entities (e.g., "da Vinci," "Mona Lisa") and relationships (e.g., "painted") of a knowledge graph (KG) as vectors. KGEs are generated by optimizing an embedding score, which assesses whether a triple (e.g., "da Vinci," "painted," "Mona Lisa") exists in the graph. KGEs have been proven effective in a variety of web-related downstream tasks, including, for instance, predicting relationships among entities. However, the problem of anticipating the performance of a given KGE in a certain downstream task and locally to a specific individual triple, has not been tackled so far. In this paper, we fill this gap with ReliK, a Reliability measure for KGEs. ReliK relies solely on KGE embedding scores, is task- and KGE-agnostic, and requires no further KGE training. As such, it is particularly appealing for semantic web applications which call for testing multiple KGE methods on various parts of the KG and on each individual downstream task. Through extensive experiments, we attest that ReliK correlates well with both common downstream tasks, such as tail or relation prediction and triple classification, as well as advanced downstream tasks, such as rule mining and question answering, while preserving locality.