health informatics teams

Abstract: 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.

Abstract: Pulling a fixed set of fields out of documents that arrive in many formats and under drifting schemas is usually done with hand-written byte patterns, which break whenever a key is renamed, a value is reformatted, or a lookalike value appears first. We argue the cause is structural: one pattern must both describe the value and locate it among its surroundings. O-Funnel separates the two. It transcribes any XML, JSON, CSV, HTML or key-value text document into one typed tree over five constructors, gated by an oracle that rejects any capture that does not reconstruct its source. Each needed field is declared in the tree's own terms and located by fusing independent evidence (key, path, value shape, synonym, key spelling, record neighborhood, value profile), so the best-supported node wins and a missing field is reported with a reason. Data no requirement claims becomes residue that a funnel traces back to the requirements to learn new key aliases. On 34,989 real PubMed records, O-Funnel matches a hand-written parser (F1 1.00). After a five-element schema rename, the parser's regular expressions fall to 0.20 while O-Funnel stays at 1.00, with every capture verified complete. On constructed suites that isolate regex failure modes it raises F1 from 0.43 to 1.00, and from 0.80 to 0.94 after self-improvement; on held-out schema-matching instances it is competitive with classical matchers without training. O-Funnel is a dependency-free Python library (pip install ofunnel).