# Putting RDF2vec in Order

arXiv: https://arxiv.org/abs/2108.05280 · HTML: https://mlsanity.com/paper/2108.05280/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.07659/) (2020)
- [Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs](https://mlsanity.com/paper/2009.04404/) (2020)
- [More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.00318/) (2020)

## Built on by (most cited first, 8 of 8)

- [pyRDF2Vec: A Python Implementation and Extension of RDF2Vec](https://mlsanity.com/paper/2205.02283/) (2022)
- [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)
- [The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings](https://mlsanity.com/paper/2207.06014/) (2022)
- [RDF-star2Vec: RDF-star Graph Embeddings for Data Mining](https://mlsanity.com/paper/2312.15626/) (2023)
- [Ontology Matching Through Absolute Orientation of Embedding Spaces](https://mlsanity.com/paper/2204.04040/) (2022)
- [Biomedical Knowledge Graph Embeddings with Negative Statements](https://mlsanity.com/paper/2308.03447/) (2023)
- [gpuRDF2vec -- Scalable GPU-based RDF2vec](https://mlsanity.com/paper/2508.01073/) (2025)

## Abstract (arXiv)

The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue that this might be a shortcoming when training RDF2vec, and show that using a word2vec variant which respects order yields considerable performance gains especially on tasks where entities of different classes are involved.