# Incorporating Literals into Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/1802.00934 · HTML: https://mlsanity.com/paper/1802.00934/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2018.

## Built on by (most cited first, 29 of 29)

- [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023)
- [GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale](https://mlsanity.com/paper/2108.13092/) (2021)
- [Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes](https://mlsanity.com/paper/2105.08683/) (2021)
- [HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations](https://mlsanity.com/paper/2108.05774/) (2021)
- [Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction](https://mlsanity.com/paper/2302.02601/) (2023)
- [Augmenting Knowledge Graphs for Better Link Prediction](https://mlsanity.com/paper/2203.13965/) (2022)
- [LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals](https://mlsanity.com/paper/2304.14742/) (2023)
- [BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs](https://mlsanity.com/paper/2306.03606/) (2023)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals](https://mlsanity.com/paper/2201.09555/) (2022)
- [Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals](https://mlsanity.com/paper/2309.03023/) (2023)
- [Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning](https://mlsanity.com/paper/2309.03219/) (2023)
- [Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks](https://mlsanity.com/paper/2303.01140/) (2023)
- [End-to-End Learning on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2309.01169/) (2023)
- [Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model](https://mlsanity.com/paper/2312.01837/) (2023)
- [Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case](https://mlsanity.com/paper/2308.01105/) (2023)
- [BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting](https://mlsanity.com/paper/2404.08322/) (2024)
- [Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets](https://mlsanity.com/paper/2407.18241/) (2024)
- [Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction](https://mlsanity.com/paper/2312.09744/) (2023)
- [NumCoKE: Ordinal-Aware Numerical Reasoning over Knowledge Graphs with Mixture-of-Experts and Contrastive Learning](https://mlsanity.com/paper/2411.12950/) (2024)
- [Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products](https://mlsanity.com/paper/2501.05768/) (2025)
- [Predicting clinical outcomes from patient care pathways represented with temporal knowledge graphs](https://mlsanity.com/paper/2502.21138/) (2025)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Adversarial Robustness of Representation Learning for Knowledge Graphs](https://mlsanity.com/paper/2210.00122/) (2022)
- [PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing](https://mlsanity.com/paper/2308.08371/) (2023)
- [Representation Learning of Complex Assemblies, An Effort to Improve Corporate Scope 3 Emissions Calculation](https://mlsanity.com/paper/2409.03769/) (2024)
- [VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2505.11803/) (2025)
- [Parameter Averaging in Link Prediction](https://mlsanity.com/paper/2510.25361/) (2025)
- [Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/2608.26729/) (2026)

## Abstract (arXiv)

Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities. In this work, we study the effect of incorporating literal information into existing link prediction methods. Our approach, which we name LiteralE, is an extension that can be plugged into existing latent feature methods. LiteralE merges entity embeddings with their literal information using a learnable, parametrized function, such as a simple linear or nonlinear transformation, or a multilayer neural network. We extend several popular embedding models based on LiteralE and evaluate their performance on the task of link prediction. Despite its simplicity, LiteralE proves to be an effective way to incorporate literal information into existing embedding based methods, improving their performance on different standard datasets, which we augmented with their literals and provide as testbed for further research.