# A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?

arXiv: https://arxiv.org/abs/1910.12507 · HTML: https://mlsanity.com/paper/1910.12507/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 30 of 31)

- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [Contextual Semantic Embeddings for Ontology Subsumption Prediction](https://mlsanity.com/paper/2202.09791/) (2022)
- [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023)
- [Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models](https://mlsanity.com/paper/2302.06229/) (2023)
- [RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs](https://mlsanity.com/paper/2211.11407/) (2022)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations](https://mlsanity.com/paper/2108.05774/) (2021)
- [Augmenting Knowledge Graphs for Better Link Prediction](https://mlsanity.com/paper/2203.13965/) (2022)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process](https://mlsanity.com/paper/2111.13854/) (2021)
- [Knowledge Graphs Querying](https://mlsanity.com/paper/2305.14485/) (2023)
- [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)
- [Towards Semantically Enriched Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2308.00081/) (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)
- [Neurosymbolic Methods for Dynamic Knowledge Graphs](https://mlsanity.com/paper/2409.04572/) (2024)
- [Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions](https://mlsanity.com/paper/2303.18103/) (2023)
- [How Contentious Terms About People and Cultures are Used in Linked Open Data](https://mlsanity.com/paper/2311.10757/) (2023)
- [Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets](https://mlsanity.com/paper/2407.18241/) (2024)
- [ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals](https://mlsanity.com/paper/2504.00852/) (2025)
- [Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?](https://mlsanity.com/paper/2301.12929/) (2023)
- [NALA: an Effective and Interpretable Entity Alignment Method](https://mlsanity.com/paper/2404.11968/) (2024)
- [AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning](https://mlsanity.com/paper/2407.18735/) (2024)
- [Predicting clinical outcomes from patient care pathways represented with temporal knowledge graphs](https://mlsanity.com/paper/2502.21138/) (2025)
- [GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs](https://mlsanity.com/paper/2504.17099/) (2025)
- [VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2505.11803/) (2025)

## Abstract (arXiv)

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also the unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.