# Duplication Detection in Knowledge Graphs: Literature and Tools

arXiv: https://arxiv.org/abs/2004.08257 · HTML: https://mlsanity.com/paper/2004.08257/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [LINK-KG: LLM-Driven Coreference-Resolved Knowledge Graphs for Human Smuggling Networks](https://mlsanity.com/paper/2510.26486/) (2025)
- [Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs](https://mlsanity.com/paper/2510.26512/) (2025)
- [CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks](https://mlsanity.com/paper/2506.21607/) (2025)

## Abstract (arXiv)

In recent years, an increasing amount of knowledge graphs (KGs) have been created as a means to store cross-domain knowledge and billion of facts, which are the basis of costumers' applications like search engines. However, KGs inevitably have inconsistencies such as duplicates that might generate conflicting property values. Duplication detection (DD) aims to identify duplicated entities and resolve their conflicting property values effectively and efficiently. In this paper, we perform a literature review on DD methods and tools, and an evaluation of them. Our main contributions are a performance evaluation of DD tools in KGs, improvement suggestions, and a DD workflow to support future development of DD tools, which are based on desirable features detected through this study.