• Facets (new session)
• Description
• Metadata
• Settings
  • Rule: ActivityStreamsMapasEquivalentb3sb3sifpdbprdf-labelfacetshttp://dbpedia.org/resource/inference/rules/dbpedia#http://dbpedia.org/resource/inference/rules/opencyc#http://dbpedia.org/resource/inference/rules/umbel#http://dbpedia.org/resource/inference/rules/yago#http://dbpedia.org/schema/property_rules#http://www.ontologyportal.org/inference/rules/SUMO#http://www.ontologyportal.org/inference/rules/WordNet#http://www.w3.org/2002/07/owl#ldpoplwebskos-transurn:det:rdf:labelvirtrdf-labelvirtrdf-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)

About: Data hierarchy     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : owl:Thing, within Data Space : dbpedia.org associated with source document(s)

Data hierarchy refers to the systematic organization of data, often in a hierarchical form. Data organization involves characters, fields, records, files and so on. This concept is a starting point when trying to see what makes up data and whether data has a structure. For example, how does a person make sense of data such as 'employee', 'name', 'department', 'Marcy Smith', 'Sales Department' and so on, assuming that they are all related? One way to understand them is to see these terms as smaller or larger components in a hierarchy. One might say that Marcy Smith is one of the employees in the Sales Department, or an example of an employee in that Department. The data we want to capture about all our employees, and not just Marcy, is the name, ID number, address etc.