# Order Matters: Matching Multiple Knowledge Graphs

arXiv: https://arxiv.org/abs/2111.02239 · HTML: https://mlsanity.com/paper/2111.02239/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2021.

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

- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching](https://mlsanity.com/paper/2209.07479/) (2022)
- [CMOMgen: Complex Multi-Ontology Alignment via Pattern-Guided In-Context Learning](https://mlsanity.com/paper/2510.21656/) (2025)

## Abstract (arXiv)

Knowledge graphs (KGs) provide information in machine interpretable form. In cases where multiple KGs are used in the same system, that information needs to be integrated. This is usually done by automated matching systems. Most of those systems consider only 1:1 (binary) matching tasks. Thus, matching a larger number of knowledge graphs with such systems would lead to quadratic efforts. In this paper, we empirically analyze different approaches to reduce the task of multi-source matching to a linear number of executions of binary matching systems. We show that the matching order of KGs and the multi-source strategy actually matter and that near-optimal results can be achieved with linear efforts.