mlsanityData to 7 Oct 2026

Putting RDF2vec in Order

arXiv 2108.05280 ↗First posted 11 Aug 2021cs.LG cs.AIcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 11 Aug 2021.

Builds on

8 of 15 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  5. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 323
  6. 2020RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddingscited by 16
  7. 2020Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphscited by 8
  8. 2020More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddingscited by 2

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Cited by year

Citing papers per year: 2022: 5; 2023: 2; 2024: 0; 2025: 1.

8 papers.