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#rdf2vec

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FIZ ISE Research Group

Poster from our colleague @epoz from UGent-IMEC Linked Data & Solid course. "Exploding Mittens - Getting to grips with huge SKOS datasets" on semantic embeddings enhanced SPARQL queries for ICONCLASS data.
Congrats for the 'best poster' award ;-)

poster: zenodo.org/records/14887544
iconclass on GitHub: github.com/iconclass

#rdf2vec #bert #llm #embeddings #iconclass #semanticweb #lod #linkeddata #knowledgegraphs #dh @nfdi4culture @fiz_karlsruhe #iconclass

Very nice poster "Getting the Grips with huge SKOS datasets" - Exploding mittens. Exploding Mittens - Getting to grips with huge SKOS datasets Creators: Posthumus, Etienne The ICONCLASS classification system contains more than 1 million SKOS nodes in English, German, French, Italian, Portuguese, Japanese, Dutch, Polish and Czech- in short a non-trivial large corpus of Linked Data. It is possible to load the data into performant triplestores, but becomes challenging in a production setting. Query performance using conventional SPARQL constructs is unacceptable to end-users, and finding relevant search results for users queries is an informational retrieval challenge due to the redundant yet slightly variant nature of the Art Historical classification data. We created a virtual Linked Data query engine that integrates a SPARQL API with semantic embeddings using BERT based transformers and RDF2Vec knowledge graph embeddings to provide performant queries in the Dutch Netwerk Digitaal Erfgoed termen netwerk.
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