BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation
The paper introduces BLINKG, a benchmark for evaluating the capabilities of Large Language Models (LLMs) in generating Knowledge Graphs (KGs). It highlights the challenges faced by knowledge engineers in aligning data sources with ontology terms and the potential of LLMs to assist in this process. The benchmark aims to assess LLM performance across various scenarios, revealing both promising results and limitations in complex cases.
- ▪Generating Knowledge Graphs remains a labor-intensive task for knowledge engineers.
- ▪BLINKG is proposed as a benchmark to evaluate LLMs in constructing KGs from heterogeneous data sources.
- ▪The benchmark includes scenarios of increasing complexity based on real-world use cases.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19518 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.19518 (cs) [Submitted on 19 May 2026] Title:BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation Authors:Carla Castedo, Enrique Iglesias, Manuel Lama, Alberto Bugarin-Diz, Maria-Esther Vidal, David Chaves-Fraga View a PDF of the paper titled BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation, by Carla Castedo and 5 other authors View PDF HTML (experimental) Abstract:Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify semantic equivalences between input data sources and ontology terms.
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