# RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2009.07659 · HTML: https://mlsanity.com/paper/2009.07659/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [Sem@K: Is my knowledge graph embedding model semantic-aware?](https://mlsanity.com/paper/2301.05601/) (2023)
- [Putting RDF2vec in Order](https://mlsanity.com/paper/2108.05280/) (2021)
- [Entity Type Prediction Leveraging Graph Walks and Entity Descriptions](https://mlsanity.com/paper/2207.14094/) (2022)
- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)
- [Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph](https://mlsanity.com/paper/2204.13329/) (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)
- [Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks](https://mlsanity.com/paper/2303.01140/) (2023)
- [RDF-star2Vec: RDF-star Graph Embeddings for Data Mining](https://mlsanity.com/paper/2312.15626/) (2023)
- [FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs](https://mlsanity.com/paper/2103.01576/) (2021)
- [Ontology Matching Through Absolute Orientation of Embedding Spaces](https://mlsanity.com/paper/2204.04040/) (2022)
- [Synthetic Multimodal Dataset for Empowering Safety and Well-being in Home Environments](https://mlsanity.com/paper/2401.14743/) (2024)
- [On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia](https://mlsanity.com/paper/2107.00873/) (2021)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)

## Abstract (arXiv)

Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes and edges. This leads to very high computational requirements on large graphs such as DBpedia or Wikidata. However, for most downstream application scenarios, only a small subset of concepts is of actual interest. In this paper, we present RDF2Vec Light, a lightweight embedding approach based on RDF2Vec which generates vectors for only a subset of entities. To that end, RDF2Vec Light only traverses and processes a subgraph of the knowledge graph. Our method allows the application of embeddings of very large knowledge graphs in scenarios where such embeddings were not possible before due to a significantly lower runtime and significantly reduced hardware requirements.