• Facets (new session)
• Description
• Metadata
• Settings
  • Rule: ActivityStreamsMapasEquivalentb3sb3sifpccisofacetshttp://demo.openlinksw.com/dataspacehttp://www.w3.org/2002/07/owl#ldpoplwebrel-me-authskos-transsql-vdb-data-lakeurn:ALKAdatareducedurn:analytics:acqof:ruleurn:clothing:rule:qaurn:det:rdf:labelurn:inference:oplfaq:ruleurn:inference:product:feature:inverse:ruleurn:ingleng:inf:demo:inf:ruleurn:kbpedia:actor:subclass:test:rulesurn:kbpedia:comedian:owl:equivalent:class:subproperty:inference:rulesurn:kbpedia:mime-artist:owl:equivalent:class:subproperty:inference:rulesurn:kbpedia:owl:equivalent:class:subproperty:inference:rulesurn:kbpedia:philosopher:owl:equivalent:class:subproperty:inference:rulesurn:kbpedia:stage-actor:owl:equivalent:class:subproperty:inference:rulesurn:kgdemo:1:ifp:ruleurn:kgdemo:1:ifp:rule2urn:lava:demo:ruleurn:owl:equivalent:class:inference:rulesurn:rdfs:subclass:subproperty:inference:rulesurn:rdfs:type:wikidata:ruleurn:recon:data:ifpurn:recon:data:ifp:ruleurn:recruitment:rule:built-in:ruleurn:rsm12e:translation:ruleurn:rxnorm:data:ruleurn:se:demo:inference:ruleurn:test:skos:inverse:ruleurn:wireless:plans:features:builtin:inference:rule:1virtrdf-eturtle-containersvirtrdf-hashtagvirtrdf-ifpvirtrdf-labelvirtrdf-meta-entity-classvirtrdf-urlNone
    
  • Inverse Functional Properties: Disabled (fastest) Apply to subjects only Apply to objects only Apply to both subjects and objects
  • "Same As": Disabled (fastest) Apply to subjects only Apply to objects only Apply to both subjects and objects (recommended) Apply to subjects, objects and predicates (not recommended on big datasets) Apply to predicates only (special use cases only)

The Virtuoso 08.03.3326+ engine has been enhanced to use 64-bit prefix IDs in RDF_IRI which allows for even larger databases. This enhancement fixes two important problems in Virtuoso: 1. When Virtuoso was upgraded to use 64-bit IRI_IDs around v6.0.x, we forgot to upgrade the RDF_IRI table to use a 64-bit prefix ID. This meant that Virtuoso could only store around 2 billion distinct prefixes before returning an error. 2. The algorithm to generate distinct prefixes resulted in too many prefixes being created. While this is not a problem for storing small or even medium sized data sets, this becomes a problem when you want to host large databases the size of Uniprot which now contains over 90 billion triples