# Duplicate Detection as a Service

arXiv: https://arxiv.org/abs/2207.09672 · HTML: https://mlsanity.com/paper/2207.09672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Plagiarism: Taxonomy, Tools and Detection Techniques](https://mlsanity.com/paper/1801.06323/) (2018)
- [Domain Specific Semantic Validation of Schema.org Annotations](https://mlsanity.com/paper/1706.06384/) (2017)

## Abstract (arXiv)

Completeness of a knowledge graph is an important quality dimension and factor on how well an application that makes use of it performs. Completeness can be improved by performing knowledge enrichment. Duplicate detection aims to find identity links between the instances of knowledge graphs and is a fundamental subtask of knowledge enrichment. Current solutions to the problem require expert knowledge of the tool and the knowledge graph they are applied to. Users might not have this expert knowledge. We present our service-based approach to the duplicate detection task that provides an easy-to-use no-code solution that is still competitive with the state-of-the-art and has recently been adopted in an industrial context. The evaluation will be based on several frequently used test scenarios.