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KDAI2026 lecture covers SPARQL, DBpedia vs Wikidata, and OWL

This item details lecture number 11 of KDAI2026, focusing on SPARQL interrogation tactics. The lecture covered FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates. It also included a comparison of DBpedia and Wikidata, noting DBpedia's 1.32 billion triples derived from Wikipedia infoboxes and Wikidata's 17.6 billion triples maintained by approximately 29,000 editors. The session concluded with a discussion of OWL Description Logic SROIQ(D). AI

IMPACT Provides insights into knowledge graph querying and data comparison relevant to AI development.

RANK_REASON The item describes a lecture covering technical aspects of knowledge graphs and query languages, fitting the research category. [lever_c_demoted from research: ic=1 ai=0.7]

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KDAI2026 lecture covers SPARQL, DBpedia vs Wikidata, and OWL

COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    # KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL. Then a bounty

    # KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL. Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors) Closing o…