Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917276463136768 |
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| author | Diliso, Ivan Barile, Roberto d'Amato, Claudia Fanizzi, Nicola |
| author_facet | Diliso, Ivan Barile, Roberto d'Amato, Claudia Fanizzi, Nicola |
| contents | Datasets for the experimental evaluation of knowledge graph refinement algorithms typically contain only ground facts, retaining very limited schema level knowledge even when such information is available in the source knowledge graphs. This limits the evaluation of methods that rely on rich ontological constraints, reasoning or neurosymbolic techniques and ultimately prevents assessing their performance in large-scale, real-world knowledge graphs. In this paper, we present \resource{} the first resource that provides a workflow for extracting datasets including both schema and ground facts, ready for machine learning and reasoning services, along with the resulting curated suite of datasets. The workflow also handles inconsistencies detected when keeping both schema and facts and also leverage reasoning for entailing implicit knowledge. The suite includes newly extracted datasets from KGs with expressive schemas while simultaneously enriching existing datasets with schema information. Each dataset is serialized in OWL making it ready for reasoning services. Moreover, we provide utilities for loading datasets in tensor representations typical of standard machine learning libraries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14795 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs Diliso, Ivan Barile, Roberto d'Amato, Claudia Fanizzi, Nicola Artificial Intelligence Machine Learning 68T01 I.2.0 Datasets for the experimental evaluation of knowledge graph refinement algorithms typically contain only ground facts, retaining very limited schema level knowledge even when such information is available in the source knowledge graphs. This limits the evaluation of methods that rely on rich ontological constraints, reasoning or neurosymbolic techniques and ultimately prevents assessing their performance in large-scale, real-world knowledge graphs. In this paper, we present \resource{} the first resource that provides a workflow for extracting datasets including both schema and ground facts, ready for machine learning and reasoning services, along with the resulting curated suite of datasets. The workflow also handles inconsistencies detected when keeping both schema and facts and also leverage reasoning for entailing implicit knowledge. The suite includes newly extracted datasets from KGs with expressive schemas while simultaneously enriching existing datasets with schema information. Each dataset is serialized in OWL making it ready for reasoning services. Moreover, we provide utilities for loading datasets in tensor representations typical of standard machine learning libraries. |
| title | Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs |
| topic | Artificial Intelligence Machine Learning 68T01 I.2.0 |
| url | https://arxiv.org/abs/2602.14795 |