Nanopublication

ID

https://w3id.org/np/RAlfXJ2sQQhZ822PvzWBJiRIKkSu49odFWq5b8o7n5-To
Formats

Content

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@prefix sub: <https://w3id.org/np/RAlfXJ2sQQhZ822PvzWBJiRIKkSu49odFWq5b8o7n5-To/> .
@prefix schema: <http://schema.org/> .
@prefix gen: <https://w3id.org/kpxl/gen/terms/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix nt: <https://w3id.org/np/o/ntemplate/> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .

sub:Head {
  this: a np:Nanopublication;
    np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo .
}

sub:assertion {
  <https://w3id.org/spaces/semantics/2026-eu/symgenai4sci> a gen:Event, gen:Space, <https://w3id.org/scholarlydata/ontology/conference-ontology.owl#Workshop>;
    dct:description "The rapid advancement of Generative Artificial Intelligence (GenAI) has revolutionized various fields, including natural language processing, computer vision, and creative industries. Models such as GPT, DALL·E, and others have demonstrated the immense potential of generative AI in creating realistic, coherent, and often groundbreaking outputs across a wide range of domains. However, the application of these generative models to scientific discovery and problemsolving remains an emerging area, filled with opportunities and challenges. Science is inherently generative: it involves creating hypotheses, designing experiments, interpreting data, and communicating results. Generative AI has the potential to augment these processes by automating routine tasks, enabling hypothesis generation, synthesizing complex information, and uncovering insights from vast datasets. Scientific data is often sparse, noisy, and domain-specific, requiring models to understand and adhere to rigorous scientific principles and constraints. Issues such as model interpretability, reproducibility, and ethical use are particularly critical in scientific applications, where errors or biases can have far-reaching consequences.";
    schema:endDate "2026-09-15T14:10:00+02:00"^^xsd:dateTime;
    schema:location "Anatomisch Theater";
    schema:startDate "2026-09-15T10:20:00+02:00"^^xsd:dateTime;
    rdfs:label "SymGenAI4Sci 2026: 2nd International Workshop on Symbolic and Generative AI for Science";
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sub:provenance {
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sub:pubinfo {
  <https://orcid.org/0000-0001-9488-1870> foaf:name "Daniel Mietchen" .
  
  <https://orcid.org/0000-0002-1267-0234> foaf:name "Tobias Kuhn" .
  
  this: dct:created "2026-08-31T14:53:08Z"^^xsd:dateTime;
    dct:creator <https://w3id.org/np/RA0z3VTaQLNRhIoFyW7C8fJ04tho6xUivHyFqeY-RUv30/event-bot>;
    dct:license <https://creativecommons.org/licenses/by/4.0/>;
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    npx:supersedes <https://w3id.org/np/RA0kEG1YkS8J_aSPJFSyHz6s38V_k9MEH92M1yl-Rj2Ks>;
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