# Supervised Typing of Big Graphs using Semantic Embeddings

arXiv: https://arxiv.org/abs/1703.07805 · HTML: https://mlsanity.com/paper/1703.07805/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Leveraging Wikidata's edit history in knowledge graph refinement tasks](https://mlsanity.com/paper/2210.15495/) (2022)
- [Named Entity Resolution in Personal Knowledge Graphs](https://mlsanity.com/paper/2307.12173/) (2023)
- [Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs](https://mlsanity.com/paper/2412.17438/) (2024)
- [A Knowledge Engineering Primer](https://mlsanity.com/paper/2305.17196/) (2023)
- [A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain](https://mlsanity.com/paper/2310.05258/) (2023)

## Abstract (arXiv)

We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15x speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.