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KDAI2026 lecture covers SPARQL, DBpedia vs Wikidata, and OWL

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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]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

KDAI2026 lecture covers SPARQL, DBpedia vs Wikidata, and OWL

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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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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…

    LINKS wisskomm.social/@fiz_karlsruhe social.kit.edu/@KIT_Karlsruhe

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