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SCODEL: Shared Canonical Ontology Definitions for Food Entity Linking and Ontology-Grounded Recipe Knowledge Graphs — arXiv:2610.27637

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.27637
License (as archived): CC0-1.0
Archived: 2026-10-21T07:06:43.037Z
SHA-256 of the archived text: f6aa2ad1a8f67f8b2ec48011d5c2d131e6dbf78beaa1f3135d4e0cd477cbec4a
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How to cite

SCODEL: Shared Canonical Ontology Definitions for Food Entity Linking and Ontology-Grounded Recipe Knowledge Graphs — arXiv:2610.27637 — arXiv (en), https://arxiv.org/abs/2610.27637 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c69738bbde01 (Metraipolis archive dc_c69738bbde01, sha256 f6aa2ad1a8f67f8b2ec48011d5c2d131e6dbf78beaa1f3135d4e0cd477cbec4a)

BibTeX

@misc{dcc69738bbde01,
  title = {SCODEL: Shared Canonical Ontology Definitions for Food Entity Linking and Ontology-Grounded Recipe Knowledge Graphs — arXiv:2610.27637},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.27637},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c69738bbde01; sha256 f6aa2ad1a8f67f8b2ec48011d5c2d131e6dbf78beaa1f3135d4e0cd477cbec4a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.27637. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Identifying recipe entities and linking them to ontology concepts is fundamental for semantic recipe understanding. However, food mentions in recipes are often ambiguous, context-dependent, and lexically diverse, making ontology entity linking (EL) challenging. We propose SCODEL, a streamlined, model-agnostic approach based on a central idea: contextual food mentions and ontology concepts that refer to the same entity should learn the same canonical ontology-oriented definition. SCODEL uses these shared definitions as a semantic normalisation layer, providing complementary semantic information to existing EL models without requiring a new linking architecture. We further introduce ReciLinkD, the first benchmark for contextual food ontology entity linking in recipes. Experiments show that SCODEL consistently improves three modular EL models (ReFinED, GSM-EL, and BLINK): SCODEL x BLINK achieves the highest Hit@1 on seen concepts (88.33%), while SCODEL x GSM-EL achieves the highest Hit@1 on unseen concepts (83.15%), outperforming the strongest large language model (LLM) based EL baseline in each setting. Experiments on a biomedical ontology benchmark further demonstrate that SCODEL generalises beyond the food domain. Finally, we use SCODEL to ground food mentions at scale and construct an ontology-grounded recipe knowledge graph, demonstrating its utility for ontology-aware recipe retrieval and corpus-level food analysis.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.27637 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-21T07:05:43.429Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).