# Entity Type Prediction in Knowledge Graphs using Embeddings

arXiv: https://arxiv.org/abs/2004.13702 · HTML: https://mlsanity.com/paper/2004.13702/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Entity Type Prediction Leveraging Graph Walks and Entity Descriptions](https://mlsanity.com/paper/2207.14094/) (2022)
- [Discovering Fine-Grained Semantics in Knowledge Graph Relations](https://mlsanity.com/paper/2202.08917/) (2022)
- [OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion](https://mlsanity.com/paper/2603.11820/) (2026)

## Abstract (arXiv)

Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.