# KGvec2go -- Knowledge Graph Embeddings as a Service

arXiv: https://arxiv.org/abs/2003.05809 · HTML: https://mlsanity.com/paper/2003.05809/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Entity Type Prediction Leveraging Graph Walks and Entity Descriptions](https://mlsanity.com/paper/2207.14094/) (2022)
- [Walk this Way! Entity Walks and Property Walks for RDF2vec](https://mlsanity.com/paper/2204.02777/) (2022)
- [Background Knowledge in Schema Matching: Strategy vs. Data](https://mlsanity.com/paper/2107.00001/) (2021)
- [The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings](https://mlsanity.com/paper/2207.06014/) (2022)
- [FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs](https://mlsanity.com/paper/2103.01576/) (2021)
- [Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings](https://mlsanity.com/paper/2509.07905/) (2025)

## Abstract (arXiv)

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.