Schema Generation for Large Knowledge Graphs Using Large Language Models
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916984099176448 |
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| author | Zhang, Bohui He, Yuan Pintscher, Lydia Peñuela, Albert Meroño Simperl, Elena |
| author_facet | Zhang, Bohui He, Yuan Pintscher, Lydia Peñuela, Albert Meroño Simperl, Elena |
| contents | Schemas play a vital role in ensuring data quality and supporting usability in the Semantic Web and natural language processing. Traditionally, their creation demands substantial involvement from knowledge engineers and domain experts. Leveraging the impressive capabilities of large language models (LLMs) in tasks like ontology engineering, we explore schema generation using LLMs. To bridge the resource gap, we introduce two datasets: YAGO Schema and Wikidata EntitySchema, along with novel evaluation metrics. The LLM-based pipelines utilize local and global information from knowledge graphs (KGs) to generate schemas in Shape Expressions (ShEx). Experiments demonstrate LLMs' strong potential in producing high-quality ShEx schemas, paving the way for scalable, automated schema generation for large KGs. Furthermore, our benchmark introduces a new challenge for structured generation, pushing the limits of LLMs on syntactically rich formalisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04512 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Schema Generation for Large Knowledge Graphs Using Large Language Models Zhang, Bohui He, Yuan Pintscher, Lydia Peñuela, Albert Meroño Simperl, Elena Artificial Intelligence Schemas play a vital role in ensuring data quality and supporting usability in the Semantic Web and natural language processing. Traditionally, their creation demands substantial involvement from knowledge engineers and domain experts. Leveraging the impressive capabilities of large language models (LLMs) in tasks like ontology engineering, we explore schema generation using LLMs. To bridge the resource gap, we introduce two datasets: YAGO Schema and Wikidata EntitySchema, along with novel evaluation metrics. The LLM-based pipelines utilize local and global information from knowledge graphs (KGs) to generate schemas in Shape Expressions (ShEx). Experiments demonstrate LLMs' strong potential in producing high-quality ShEx schemas, paving the way for scalable, automated schema generation for large KGs. Furthermore, our benchmark introduces a new challenge for structured generation, pushing the limits of LLMs on syntactically rich formalisms. |
| title | Schema Generation for Large Knowledge Graphs Using Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.04512 |