# Evaluation of Pipelines for Data Integration into Knowledge Graphs

arXiv: https://arxiv.org/abs/2605.22304 · HTML: https://mlsanity.com/paper/2605.22304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 14 of 14)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Valentine: Evaluating Matching Techniques for Dataset Discovery](https://mlsanity.com/paper/2010.07386/) (2020)
- [PG-Schema: Schemas for Property Graphs](https://mlsanity.com/paper/2211.10962/) (2022)
- [The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias](https://mlsanity.com/paper/2002.10283/) (2020)
- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)
- [ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources](https://mlsanity.com/paper/2210.14846/) (2022)
- [Towards Knowledge Graphs Validation through Weighted Knowledge Sources](https://mlsanity.com/paper/2104.12622/) (2021)
- [Steps to Knowledge Graphs Quality Assessment](https://mlsanity.com/paper/2208.07779/) (2022)
- [KGpipe: Generation and Evaluation of Pipelines for Data Integration into Knowledge Graphs](https://mlsanity.com/paper/2511.18364/) (2025)
- [Is SHACL Suitable for Data Quality Assessment?](https://mlsanity.com/paper/2507.22305/) (2025)
- [Benchmarking Large Language Models for Knowledge Graph Validation](https://mlsanity.com/paper/2602.10748/) (2026)

## Built on by (most cited first, 1 of 1)

- [MaDI-Bench: An End-to-End Data Integration Benchmark](https://mlsanity.com/paper/2606.30371/) (2026)

## Abstract (arXiv)

Integrating new data into knowledge graphs (KG) typically involves different tasks that are executed within workflows or pipelines There are many possible pipelines for a specific integration problem but there is not yet a general approach to evaluate the overall quality and performance of such pipelines to be able to determine the best choices. We therefore propose a new benchmark KGI-Bench to evaluate integration pipelines that ingest different kinds of input data into an existing KG. We evaluate pipelines by analyzing their output, i.e., the updated KG, with the three complementary quality metrics coverage, correctness and consistency. We also provide benchmark datasets (seed KG, overlapping input data of three formats, reference KG as a ground truth) for the movie domain. To demonstrate the applicability and usefulness of the proposed benchmark, we comparatively evaluate 12 pipelines and analyze their behavior across different input data formats and design choices.