# Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals

arXiv: https://arxiv.org/abs/2309.03023 · HTML: https://mlsanity.com/paper/2309.03023/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?](https://mlsanity.com/paper/1910.12507/) (2019)
- [Incorporating Literals into Knowledge Graph Embeddings](https://mlsanity.com/paper/1802.00934/) (2018)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [pyRDF2Vec: A Python Implementation and Extension of RDF2Vec](https://mlsanity.com/paper/2205.02283/) (2022)
- [Augmenting Knowledge Graphs for Better Link Prediction](https://mlsanity.com/paper/2203.13965/) (2022)
- [Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review](https://mlsanity.com/paper/2203.09280/) (2022)

## Built on by (most cited first, 4 of 4)

- [ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals](https://mlsanity.com/paper/2504.00852/) (2025)
- [Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction](https://mlsanity.com/paper/2404.14970/) (2024)
- [Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs](https://mlsanity.com/paper/2503.20400/) (2025)
- [Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning](https://mlsanity.com/paper/2603.19888/) (2026)

## Abstract (arXiv)

Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG). While the majority of approaches concentrate only on relational information, i.e., relations between entities, fewer approaches exist which also take information about literal values (e.g., textual descriptions or numerical information) into account. Those which exist are typically tailored towards a particular modality of literal and a particular embedding method. In this paper, we propose a set of universal preprocessing operators which can be used to transform KGs with literals for numerical, temporal, textual, and image information, so that the transformed KGs can be embedded with any method. The results on the kgbench dataset with three different embedding methods show promising results.