Dear Jona,

thanks a lot for the clarification. I was not aware of the distinction between these two sets of properties.

Best regards,
Basil

Am 12.07.2013 16:41, schrieb Jona Christopher Sahnwaldt:
http://wiki.dbpedia.org/Datasets#h18-11

Wikipedia infoboxes contain very specific information about things and are thus a very valuable source of structured information that can be used to ask expressive queries against Wikipedia. The DBpedia project currently extracts three different datasets from the Wikipedia infoboxes.

 1. The *Infobox Dataset* is created using our initial, now three year
    old infobox parsing approach. This extractor extracts
    all properties from all infoboxes and templates within
    all Wikipedia articles. Extracted information is represented using
    properties in thehttp://dbpedia.org/property/ namespace. The names
    of the these properties directly reflect the name of the Wikipedia
    infobox property. Property names are not cleaned or merged.
    Property types are not part of a subsumption hierarchy and there
    is no consistent ontology for the infobox dataset. Currently,
    there are approximately 8000 different property types. The infobox
    extractor performs only a minimal amount of property value
    clean-up, e.g., by converting a value like “June 2009” to the
    XML Schema format “2009–06”. You should therefore use the infobox
    dataset only if your application requires complete coverage of all
    Wikipeda properties and you are prepared to accept relatively
    noisy data.
 2. The *Infobox Ontology*. With the DBpedia 3.2 release,
    we introduced a new infobox extraction method which is based
    on hand-generated mappings of Wikipedia infoboxes/templates to a
    newly created DBpedia ontology <http://wiki.dbpedia.org/Ontology>.
    The mappings adjust weaknesses in the Wikipedia infobox system,
    like using different infoboxes for the same type of thing (class)
    or using different property names for the same property.
    Therefore, the instance data within the infobox ontology is much
    cleaner and better structured than the Infobox Dataset,
    but currently doesn't cover all infobox types and infobox
    properties within Wikipedia. Starting with DBpedia release 3.5,
    we provide three different Infobox Ontology data sets:
      * The *Ontology Infobox Types* dataset contains the
        rdf:types of the instances which have been extracted from
        the infoboxes.
      * The *Ontology Infobox Properties* dataset contains the actual
        data values that have been extracted from infoboxes. The data
        values are represented using ontology properties (e.g.,
        'volume') that may be applied to different things (e.g.,
        the volume of a lake and the volume of a planet). This
        restricts the number of different properties to a minimum,
        but has the drawback that it is not possible to automatically
        infer the class of an entity based on a property.
        For instance, an application that discovers an entity
        described using the volume property cannot infer that that
        the entity is a lake and then for example use a map
        to visualize the entity. Properties are represented using
        properties following the
        http://dbpedia.org/ontology/{propertyname}
        <http://dbpedia.org/ontology/%7Bpropertyname%7D> naming
        schema. All values are normalized to their respective SI unit.
      * The *Ontology Infobox Properties (Specific)* dataset contains
        properties which have been specialized for a specific class
        using a specific unit. e.g. the property height is specialized
        on the class Person using the unit centimetres instead
        of metres. Specialized properties follow the
        http://dbpedia.org/ontology/{Class}/{property}
        <http://dbpedia.org/ontology/%7BClass%7D/%7Bproperty%7D> naming schema
        (e.g.http://dbpedia.org/ontology/Person/height).
        The properties have a single class as rdfs:domain
        and rdfs:range and can therefore be used for classification
        reasoning. This makes it easier to express queries against
        the data, e.g., finding all lakes whose volume is in a certain
        range. Typically, the range of the properties are not using
        SI units, but a unit which is more appropriate in the specific
        domain.

All three data sets are available for download <http://wiki.dbpedia.org/Downloads> as well as being available for queries via the DBpedia SPARQL endpoint.

See also

http://wiki.dbpedia.org/Downloads38#ontology-infobox-properties

http://wiki.dbpedia.org/Downloads38#raw-infobox-properties




On 12 July 2013 12:06, Basil Ell <[email protected] <mailto:[email protected]>> wrote:

    Hi,

    I was wondering if there is some dataset that contains
    owl:equivalentProperty triples for dbpedia properties.

    For example, I believe that the following properties are equivalent:
    <http://dbpedia.org/ontology/subsidiary>
    <http://dbpedia.org/ontology/subsidiary>
    <http://dbpedia.org/property/subsidiary>
    <http://dbpedia.org/property/subsidiary>
    <http://dbpedia.org/property/subsid>
    <http://dbpedia.org/property/subsid>

    However, this information cannot be found in dbpedia.

    According to
      SELECT COUNT DISTINCT * WHERE {
        ?s owl:equivalentProperty ?o .
      }
    there are only 31 equivalencies.

    When querying the LOD cloud cache at http://lod.openlinksw.com/sparql,
    according to
      SELECT count distinct ?s WHERE {
        ?s owl:equivalentProperty ?o .
        FILTER(regex(?s, "dbpedia"))
      }
    there are 31 triples
    and according to
      SELECT count distinct ?s WHERE {
        ?s owl:equivalentProperty ?o .
        FILTER(regex(?o, "dbpedia"))
      }
    there are 87 triples.

    From the mappings wiki (http://mappings.dbpedia.org/) I could
    extract only 49 equivalencies.

    These numbers are rather small. Am I missing something, is there a
    data set containing these equivalencies somewhere?

    Best regards and thanks for any insights,
    Basil




    
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