# Knowledge Graphs on the Web -- an Overview

arXiv: https://arxiv.org/abs/2003.00719 · HTML: https://mlsanity.com/paper/2003.00719/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 24 of 24)

- [Sequence-to-Sequence Knowledge Graph Completion and Question Answering](https://mlsanity.com/paper/2203.10321/) (2022)
- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)
- [Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding](https://mlsanity.com/paper/2505.19825/) (2025)
- [The CaLiGraph Ontology as a Challenge for OWL Reasoners](https://mlsanity.com/paper/2110.05028/) (2021)
- [NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker](https://mlsanity.com/paper/2303.04426/) (2023)
- [Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals](https://mlsanity.com/paper/2309.03023/) (2023)
- [SnapE -- Training Snapshot Ensembles of Link Prediction Models](https://mlsanity.com/paper/2408.02707/) (2024)
- [Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation](https://mlsanity.com/paper/2505.02737/) (2025)
- [The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings](https://mlsanity.com/paper/2207.06014/) (2022)
- [Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE](https://mlsanity.com/paper/2306.03659/) (2023)
- [HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation](https://mlsanity.com/paper/2408.13521/) (2024)
- [Structural Quality Metrics to Evaluate Knowledge Graphs](https://mlsanity.com/paper/2211.10011/) (2022)
- [ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training](https://mlsanity.com/paper/2501.07078/) (2025)
- [Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2502.19023/) (2025)
- [Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy](https://mlsanity.com/paper/2101.03054/) (2021)
- [Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges](https://mlsanity.com/paper/2308.04814/) (2023)
- [Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs](https://mlsanity.com/paper/2412.17438/) (2024)
- [Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia](https://mlsanity.com/paper/2105.00674/) (2021)
- [On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia](https://mlsanity.com/paper/2107.00873/) (2021)
- [Knowledge Engineering for Wind Energy](https://mlsanity.com/paper/2310.00804/) (2023)
- [Conceptual Engineering Using Large Language Models](https://mlsanity.com/paper/2312.03749/) (2023)
- [Class Granularity: How richly does your knowledge graph represent the real world?](https://mlsanity.com/paper/2411.06385/) (2024)

## Abstract (arXiv)

Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowledge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chapter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap.