# Knowledge Graph Curation: A Practical Framework

arXiv: https://arxiv.org/abs/2208.08130 · HTML: https://mlsanity.com/paper/2208.08130/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 62 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs](https://mlsanity.com/paper/1510.05911/) (2015)
- [Finding Streams in Knowledge Graphs to Support Fact Checking](https://mlsanity.com/paper/1708.07239/) (2017)
- [Triple Trustworthiness Measurement for Knowledge Graph](https://mlsanity.com/paper/1809.09414/) (2018)
- [SURFACE: Semantically Rich Fact Validation with Explanations](https://mlsanity.com/paper/1810.13223/) (2018)
- [Towards Knowledge Graphs Validation through Weighted Knowledge Sources](https://mlsanity.com/paper/2104.12622/) (2021)

## Built on by (most cited first, 2 of 2)

- [QICHWABASE: A Quechua Language and Knowledge Base for Quechua Communities](https://mlsanity.com/paper/2305.06173/) (2023)
- [OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion](https://mlsanity.com/paper/2603.11820/) (2026)

## Abstract (arXiv)

Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility in business applications, as they are not curated, e.g., low-quality KGs produce low-quality applications that are built on top of them. In this vision paper, we propose a practical knowledge graph curation framework for improving the quality of KGs. First, we define a set of quality metrics for assessing the status of KGs, Second, we describe the verification and validation of KGs as cleaning tasks, Third, we present duplicate detection and knowledge fusion strategies for enriching KGs. Furthermore, we give insights and directions toward a better architecture for curating KGs.