Nanopublication

ID

https://w3id.org/np/RA9yD3X3X9MDBytG88d-TuyyMhXfwJjSaN2OUYubP78WQ
Formats

Content

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sub:assertion {
  <https://w3id.org/spaces/ekaw/2026/neural-networks-meet-explicit-knowledge-representation>
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    dct:description "Transformer-based Large Language Models (Tb-LLMs) demonstrate extraordinary capabilities and, thus, change the approach of conducting research even in Knowledge Representation. Building on existing research that neural networks (NNs) on discrete symbols operate as holistic reasoners over “classical” symbolic approaches, this tutorial aims to present the dominant paradigm of Tb-LLMs according to the idea that these models can be understood with neuro-symbolic, ontology-grounded approaches and controlled through mechanistic interpretable or explainable KG reasoning methods. Offering a profound understanding of the inner workings of these models, the tutorial will help practitioners build novel, transparent architectures to model symbolic knowledge in NN.";
    schema:endDate "2026-09-29T17:00:00+02:00"^^xsd:dateTime;
    schema:location "Sala Comuni";
    schema:startDate "2026-09-29T13:00:00+02:00"^^xsd:dateTime;
    rdfs:label "Neural Networks meet Explicit Knowledge Representation: Towards Mechanistic Interpretability and Neuro-symbolic Modeling by-design";
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    rdfs:label "Neural Networks meet Explicit Knowledge Representation: Towards Mechanistic Interpretability and Neuro-symbolic Modeling by-design";
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