# Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGs

arXiv: https://arxiv.org/abs/2311.06414 · HTML: https://mlsanity.com/paper/2311.06414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 26 of 26)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries](https://mlsanity.com/paper/2008.09036/) (2020)
- [Crawling the Internal Knowledge-Base of Language Models](https://mlsanity.com/paper/2301.12810/) (2023)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [SNAP: A General Purpose Network Analysis and Graph Mining Library](https://mlsanity.com/paper/1606.07550/) (2016)
- [Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling](https://mlsanity.com/paper/2306.11489/) (2023)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [CSKG: The CommonSense Knowledge Graph](https://mlsanity.com/paper/2012.11490/) (2020)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19](https://mlsanity.com/paper/2004.07229/) (2020)
- [Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey](https://mlsanity.com/paper/2110.08455/) (2021)
- [OpenBioLink: A benchmarking framework for large-scale biomedical link prediction](https://mlsanity.com/paper/1912.04616/) (2019)
- [A Review of Knowledge Graph Completion](https://mlsanity.com/paper/2208.11652/) (2022)
- [KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models](https://mlsanity.com/paper/2208.11024/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models](https://mlsanity.com/paper/2409.04103/) (2024)

## Abstract (arXiv)

Despite the recent popularity of knowledge graph (KG) related tasks and benchmarks such as KG embeddings, link prediction, entity alignment and evaluation of the reasoning abilities of pretrained language models as KGs, the structure and properties of real KGs are not well studied. In this paper, we perform a large scale comparative study of 29 real KG datasets from diverse domains such as the natural sciences, medicine, and NLP to analyze their properties and structural patterns. Based on our findings, we make several recommendations regarding KG-based model development and evaluation. We believe that the rich structural information contained in KGs can benefit the development of better KG models across fields and we hope this study will contribute to breaking the existing data silos between different areas of research (e.g., ML, NLP, AI for sciences).