Fast Linear Model for Knowledge Graph Embeddings
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Abstract (arXiv)
This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2.
4 papers.
- 2023How to Turn Your Knowledge Graph Embeddings into Generative Modelscited by 20
- 2021Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolutioncited by 12
- 2021Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysiscited by 10
- 2023Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generationcited by 0
- 2023Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation23 Jun 2023
- 2023How to Turn Your Knowledge Graph Embeddings into Generative Models25 May 2023
- 2021Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution5 Jul 2021
- 2021Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis10 Apr 2021