wiki.dbpedia.org - Datasets

2009-11-23 18:41:26

Comparison for /Datasets of 2009-11-23 18:41:26 and 2009-11-23 18:53:38

Additions:

||City | ((http://dbpedia.org/resource/Cambridge Cambridge)), ((http://dbpedia.org/resource/Berlin Berlin)), ((http://dbpedia.org/resource/Manchester Manchester)) ||
||Country |((http://dbpedia.org/resource/Spain Spain)), ((http://dbpedia.org/resource/Iceland Iceland)), ((http://dbpedia.org/resource/South_Korea South Korea)) ||
||Politician |((http://dbpedia.org/resource/George_W._Bush George W. Bush)), ((http://dbpedia.org/resource/Nicolas_Sarkozy Nicolas Sarkozy)), ((http://dbpedia.org/resource/Angela_Merkel Angela Merkel))||
||Musician | ((http://dbpedia.org/resource/AC/DC AC/DC)), ((http://dbpedia.org/resource/Diana_Ross Diana Ross)), ((http://dbpedia.org/resource/R%C3%B6yksopp Röyksopp)) ||
||Music album |((http://dbpedia.org/resource/Led_Zeppelin_III Led Zeppelin III)), ((http://dbpedia.org/resource/Like_a_Virgin Like a Virgin)), ((http://dbpedia.org/resource/Thriller_%28album%29 Thriller))||
||Director | ((http://dbpedia.org/resource/Woody_Allen Woody Allen)), ((http://dbpedia.org/resource/Oliver_Stone Oliver Stone)), ((http://dbpedia.org/resource/Takashi_Miike Takashi Miike))||
||Film | ((http://dbpedia.org/resource/Pulp_Fiction_%28film%29 Pulp Fiction)), ((http://dbpedia.org/resource/Hysterical_Blindness Hysterical Blindness)), ((http://dbpedia.org/resource/Breakfast_at_Tiffany%27s Breakfast at Tiffany's))||
||Book | ((http://dbpedia.org/resource/The_Lord_of_the_Rings The Lord of the Rings)), ((http://dbpedia.org/resource/The_Adventures_of_Tom_Sawyer_%28novel%29 The Adventures of Tom Sawyer)), ((http://dbpedia.org/resource/Bible The Holy Bible))||
||Computer Game | ((http://dbpedia.org/resource/Tetris Tetris)), ((http://dbpedia.org/resource/World_of_Warcraft World of Warcraft)), ((http://dbpedia.org/resource/Sam_&_Max_Hit_the_Road Sam & Max hit the Road))||
||Technical Standard | ((http://dbpedia.org/resource/HTML HTML)), ((http://dbpedia.org/resource/Resource_Description_Framework RDF)), ((http://dbpedia.org/resource/Uniform_Resource_Identifier URI))||

The Infobox Dataset is created using our initial, now two 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 the http://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.
- The Loose Infobox Ontology uses 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. Loose Infobox data is represented using properties following the http://dbpedia.org/ontology/{propertyname} naming schema. Property values directly use the units of measurement given in the Wikipedia article. There may be different units being used for the same property (e.g., sometimes cubic metres, sometimes cubic inches). When the property value can not be parsed, the original string value of the Wikipedia template property is used. You should therefore use the Loose Infobox Ontology if your application requires a minimal number of different properties, you don't need class reasoning and you are prepared to accept un-normalized units of measurement.
- The Strict Infobox Ontology uses different ontology properties to represent Wikipedia properties with the same name that are used to describe different things (e.g., 'Lake/volume' and 'Planet/volume'). Strict Infobox properties follow the http://dbpedia.org/ontology/{Class}/{property} naming schema. The properties have a single class as rdfs:domain and rdfs:range and can therefore be used for classification reasoning. The units of measurement are normalized, meaning that different units used in the Wikipedia templates are converted to the target units used in the ontology (for instance kilometer, meter and centimeter are all converted into meter). This makes it easier to express queries against the data, e.g., finding all lakes whose volume is in a certain range.

SELECT *
  WHERE
    {
      ?e <http://dbpedia.org/ontology/series>         <http://dbpedia.org/resource/The_Sopranos>  .
      ?e <http://dbpedia.org/ontology/releaseDate>    ?date                                       .
      ?e <http://dbpedia.org/ontology/episodeNumber>  ?number                                     .
      ?e <http://dbpedia.org/ontology/seasonNumber>   ?season
    }
  ORDER BY DESC(?date)

SELECT *
  WHERE
    { 
      ?company  a                                              <http://dbpedia.org/ontology/Organisation>  .
      ?company  <http://dbpedia.org/ontology/foundationPlace>  <http://dbpedia.org/resource/California>    .
      ?product  <http://dbpedia.org/ontology/developer>        ?company                                    .
      ?product  a                                              <http://dbpedia.org/ontology/Software>
    }

Geographical (to GeoNames.org, Eurostat data, and the RDF version of the CIA Factbook):
Authors / Books (to quotationsbook.com and Project Gutenberg RDF. Links to the RDF Book Mashup will follow soon ):
Computer Scientist publications DBLP:
- Links for States of the U.S.A.

DBpedia 3.4 data is licensed under the terms of the Creative Commons Attribution-ShareAlike 3.0 license and the GNU Free Documentation License. All DBpedia releases up to and including release 3.3 are licensed under the terms of the GNU Free Documentation License only.

Deletions:

||City | ((http://dbpedia.org/page/Cambridge Cambridge)), ((http://dbpedia.org/page/Berlin Berlin)), ((http://dbpedia.org/page/Manchester Manchester)) ||
||Country |((http://dbpedia.org/page/Spain Spain)), ((http://dbpedia.org/page/Iceland Iceland)), ((http://dbpedia.org/page/South_Korea South Korea)) ||
||Politician |((http://dbpedia.org/page/George_W._Bush George W. Bush)), ((http://dbpedia.org/page/Nicolas_Sarkozy Nicolas Sarkozy)), ((http://dbpedia.org/page/Angela_Merkel Angela Merkel))||
||Musician | ((http://dbpedia.org/page/AC/DC AC/DC)), ((http://dbpedia.org/page/Diana_Ross Diana Ross)), ((http://dbpedia.org/page/R%C3%B6yksopp Röyksopp)) ||
||Music album |((http://dbpedia.org/page/Led_Zeppelin_III Led Zeppelin III)), ((http://dbpedia.org/page/Like_a_Virgin Like a Virgin)), ((http://dbpedia.org/page/Thriller_%28album%29 Thriller))||
||Director | ((http://dbpedia.org/page/Woody_Allen Woody Allen)), ((http://dbpedia.org/page/Oliver_Stone Oliver Stone)), ((http://dbpedia.org/page/Takashi_Miike Takashi Miike))||
||Film | ((http://dbpedia.org/page/Pulp_Fiction_%28film%29 Pulp Fiction)), ((http://dbpedia.org/page/Hysterical_Blindness Hysterical Blindness)), ((http://dbpedia.org/page/Breakfast_at_Tiffany%27s Breakfast at Tiffany's))||
||Book | ((http://dbpedia.org/page/The_Lord_of_the_Rings The Lord of the Rings)), ((http://dbpedia.org/page/The_Adventures_of_Tom_Sawyer_%28novel%29 The Adventures of Tom Sawyer)), ((http://dbpedia.org/page/Bible The Holy Bible))||
||Computer Game | ((http://dbpedia.org/page/Tetris Tetris)), ((http://dbpedia.org/page/World_of_Warcraft World of Warcraft)), ((http://dbpedia.org/page/Sam_&_Max_Hit_the_Road Sam & Max hit the Road))||
||Technical Standard | ((http://dbpedia.org/page/HTML HTML)), ((http://dbpedia.org/page/Resource_Description_Framework RDF)), ((http://dbpedia.org/page/Uniform_Resource_Identifier URI))||

The Infobox Dataset is created using our initial, now two 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 the http://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.
- The Loose Infobox Ontology uses 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. Loose Infobox data is represented using properties following the http://dbpedia.org/ontology/{propertyname} naming schema. Property values directly use the units of measurement given in the Wikipedia article. There may be different units being used for the same property (e.g. sometimes cubic metres, sometimes cubic inches). When the property value can not be parsed, the original string value of the Wikipedia template property is used. You should therefore use the Loose Infobox Ontology if your application requires a minimal number of different properties, you don't need class reasoning and you are prepered to accept unnormalized units of measurement.
- The Strict Infobox Ontology uses different ontology properties to represent Wikipedia properties with the same name that are used to describe different things (e.g. 'Lake/volume' and 'Planet/volume'). Strict Infobox properties follow the http://dbpedia.org/ontology/{Class}/{property} naming schema. The properties have a single class as rdfs:domain and rdfs:range and can therefore be used for classification reasoning. The units of measurement are normalized, meaning that different units used in the Wikipedia templates are converted to the target units used in the ontology (for instance kilometer, meter and centimeter are all converted into meter). This makes it easier to express queries against the data, e.g. finding all lakes whose volume is in a certain range.

select *
where
{
?e <http://dbpedia.org/ontology/series> <http://dbpedia.org/resource/The_Sopranos> .
?e <http://dbpedia.org/ontology/releaseDate> ?date .
?e <http://dbpedia.org/ontology/episodeNumber> ?number .
?e <http://dbpedia.org/ontology/seasonNumber> ?season .
}
order by desc(?date)

select *
where
{ 
?company a <http://dbpedia.org/ontology/Organisation> .
?company <http://dbpedia.org/ontology/foundationPlace> <http://dbpedia.org/resource/California> .
?product <http://dbpedia.org/ontology/developer> ?company  .
?product a <http://dbpedia.org/ontology/Software> .
}

Geographical (to geonames.org, eurostat data and the RDF version of the CIA Factbook, both served at the FU Berlin):
Authors / Books (to quotationsbook.com and Project Gutenberg RDF, served at the FU Berlin. Links to the RDF Book Mashup will follow soon ):
Computer Scientist publications DBLP, served at the FU Berlin:
- Links for U.S. States

DBpedia 3.4 data is licensed under the terms of the Creative Commons Attribution-ShareAlike 3.0 license and the GNU Free Documentation License. All DBpedia releases upto release 3.3 are licensed unter the terms of the GNU Free Documentation License only.

2009-11-20 13:59:18

Comparison for /Datasets of 2009-11-20 13:59:18 and 2009-11-23 18:41:26

Additions:

The Infobox Dataset is created using our initial, now two 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 the http://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.

Deletions:

The Infobox Dataset is created using our initial, now two 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 the http://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.

2009-11-11 19:20:12

Comparison for /Datasets of 2009-11-11 19:20:12 and 2009-11-20 13:59:18

Additions:

The Infobox Dataset is created using our initial, now two 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 the http://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.

Deletions:

The Infobox Dataset is created using our initial, now two 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 the http://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 tpyes 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.

2009-11-11 19:04:16

Comparison for /Datasets of 2009-11-11 19:04:16 and 2009-11-11 19:20:12

Additions:

Each thing in the DBpedia data set is identified by a URI reference of the form http://dbpedia.org/resource/Name, where Name is taken from the URL of the source Wikipedia article, which has the form http://en.wikipedia.org/wiki/Name. Thus, each resource is tied directly to an English-language Wikipedia article.

Deletions:

2009-11-11 10:55:02

Comparison for /Datasets of 2009-11-11 10:55:02 and 2009-11-11 19:04:16

Additions:

The Infobox Dataset is created using our initial, now two 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 the http://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 tpyes 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.
- The Loose Infobox Ontology uses 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. Loose Infobox data is represented using properties following the http://dbpedia.org/ontology/{propertyname} naming schema. Property values directly use the units of measurement given in the Wikipedia article. There may be different units being used for the same property (e.g. sometimes cubic metres, sometimes cubic inches). When the property value can not be parsed, the original string value of the Wikipedia template property is used. You should therefore use the Loose Infobox Ontology if your application requires a minimal number of different properties, you don't need class reasoning and you are prepered to accept unnormalized units of measurement.
- The Strict Infobox Ontology uses different ontology properties to represent Wikipedia properties with the same name that are used to describe different things (e.g. 'Lake/volume' and 'Planet/volume'). Strict Infobox properties follow the http://dbpedia.org/ontology/{Class}/{property} naming schema. The properties have a single class as rdfs:domain and rdfs:range and can therefore be used for classification reasoning. The units of measurement are normalized, meaning that different units used in the Wikipedia templates are converted to the target units used in the ontology (for instance kilometer, meter and centimeter are all converted into meter). This makes it easier to express queries against the data, e.g. finding all lakes whose volume is in a certain range.

Deletions:

The Infobox Dataset is created using our initial, now two 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 the http://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 tpyes 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.
- The Loose Infobox Ontology uses 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. Loose Infobox data is represented using properties following the http://dbpedia.org/ontology/{propertyname} naming schema. Property values directly use the units of measurement given in the Wikipedia article. There may be different units being used for the same property (e.g. sometimes cubic metres, sometimes cubic inches). When the property value can not be parsed, the original string value of the Wikipedia template property is used. You should therefore use the Loose Infobox Ontology if your application requires a minimal number of different properties, you don't need class reasoning and you are prepered to accept unnormalized units of measurement.
- The Strict Infobox Ontology uses different ontology properties to represent Wikipedia properties with the same name that are used to describe different things (e.g. 'Lake/volume' and 'Planet/volume'). Strict Infobox properties follow the http://dbpedia.org/ontology/{Class}/{property} naming schema. The properties have a single class as rdfs:domain and rdfs:range and can therefore be used for classification reasoning. The units of measurement are normalized, meaning that different units used in the Wikipedia templates are converted to the target units used in the ontology (for instance kilometer, meter and centimeter are all converted into meter). This makes it easier to express queries against the data, e.g. finding all lakes whose volume is in a certain range.

2009-11-11 10:46:46

Comparison for /Datasets of 2009-11-11 10:46:46 and 2009-11-11 10:55:02

Additions:

- The Loose Infobox Ontology uses 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. Loose Infobox data is represented using properties following the http://dbpedia.org/ontology/{propertyname} naming schema. Property values directly use the units of measurement given in the Wikipedia article. There may be different units being used for the same property (e.g. sometimes cubic metres, sometimes cubic inches). When the property value can not be parsed, the original string value of the Wikipedia template property is used. You should therefore use the Loose Infobox Ontology if your application requires a minimal number of different properties, you don't need class reasoning and you are prepered to accept unnormalized units of measurement.
- The Strict Infobox Ontology uses different ontology properties to represent Wikipedia properties with the same name that are used to describe different things (e.g. 'Lake/volume' and 'Planet/volume'). Strict Infobox properties follow the http://dbpedia.org/ontology/{Class}/{property} naming schema. The properties have a single class as rdfs:domain and rdfs:range and can therefore be used for classification reasoning. The units of measurement are normalized, meaning that different units used in the Wikipedia templates are converted to the target units used in the ontology (for instance kilometer, meter and centimeter are all converted into meter). This makes it easier to express queries against the data, e.g. finding all lakes whose volume is in a certain range.

Deletions:

- The Loose Infobox Ontology uses 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. Loose Infobox data is represented using properties following the http://dbpedia.org/ontology/{propertyname} schema. Property values directly use the physical unit given in the Wikipedia template, which may be inconsistent (e.g. sometimes cubic metres, sometimes cubic inches). When the property value can not be parsed, the original string value of the template property is used. You should therefore use the infobox dataset if your application requires a minimal number of different properties, you don't need reasoning and you are prepered to accept unnormalized units.
- The Strict Infobox Ontology uses different properties for properties with the same name on different things (e.g. 'Lake/volume' and 'Planet/volume'). Strict Infobox properties follow the http://dbpedia.org/ontology/{Class}/{property} naming schema. The properties have a single class as domain and range and can therefore more easily be used for automated reasoning. The units of measurement are normalized, meaning that different units used in the Wikipedia templates are converted to the target units used in the ontology (for instance kilometer, meter and centimeter are all converted into meter). This makes it easier to express queries against the data, e.g. finding all lakes whose volume is in a certain range.

2009-11-11 10:38:13

Comparison for /Datasets of 2009-11-11 10:38:13 and 2009-11-11 10:46:46

Additions:

The Infobox Dataset is created using our initial, now two 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 the http://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 tpyes 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.
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. The ontology consists of 205 classes which form a subsumption hierarchy and have altogether 1200 properties. The mappings adjust weaknesses in the Wikipedia infobox system, like have different infoboxes for the same 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 3.4, we provide two different Infobox Ontology data sets:

Deletions:

The Infobox Dataset is created using our initial, now two 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 the http://dbpedia.org/property/ namespace. The names of the these properties directly reflect the name of the WIkipedia infobox property. Property names are not cleaned/merged and the there is no formal ontology used. Currently, there are approximately 8000 different property types. The infobox extractor also only performs 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.
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. The ontology consists of 200 classes which form a subsumption hierarchy and have altogether 1000 properties. The mappings adjust weaknesses in the Wikipedia infobox system, like have different infoboxes for the same class of thing 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 previous one, but it currently doesn't cover the whole range of infoboxes and infobox properties within Wikipedia. Starting with DBpedia release 3.4, we provide two data sets generated by this extractor:

2009-11-11 01:31:27

Comparison for /Datasets of 2009-11-11 01:31:27 and 2009-11-11 10:38:13

Additions:

The Infobox Dataset is created using our initial, now two 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 the http://dbpedia.org/property/ namespace. The names of the these properties directly reflect the name of the WIkipedia infobox property. Property names are not cleaned/merged and the there is no formal ontology used. Currently, there are approximately 8000 different property types. The infobox extractor also only performs 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.

Deletions:

The Infobox Dataset is created using our initial, now two year old infobox parsing approach. This extractor extracts all properties from all infoboxes and templates within all articles. Extracted information is represented using properties in the http://dbpedia.org/property/ namespace. The names of the these properties directly reflect the name of the WIkipedia infobox property. Property names are not cleaned/merged and the there is no formal ontology used. Currently, there are approximately 8000 different property types. The infobox extractor also only performs 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.

2009-11-11 00:00:30

Comparison for /Datasets of 2009-11-11 00:00:30 and 2009-11-11 01:31:27

Additions:

?company <http://dbpedia.org/ontology/foundationPlace> <http://dbpedia.org/resource/California> .
SPARQL Result

Deletions:

?company <http://dbpedia.org/ontology/foundationplace> <http://dbpedia.org/resource/California> .
SPARQL Result