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Dbpedia Datasets

WHAT IS Dbpedia?
It is a project idea aiming to extract structured content from the information created in the wikipedia project. This structured information is made available on the World Wide Web.
DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datsets.



BUT?

But why i am talking about Dbpedia? How it is related to natural language processing?
The DBpedia data set contains 4.58 million entities, out of which 4.22 million are classified in a consistent ontology, including 1,445,000 persons, 735,000 places, 123,000 music albums, 87,000 films, 19,000 video games, 241,000 organizations, 251,000 species and 6,000 diseases.
The data set features labels and abstracts for these entities in up to 125 languages; 25.2 million links to images and 29.8 million links to external web pages. In addition, it contains around 50 million links to other RDF datasets, 80.9 million links to Wikipedia categories, and 41.2 million YAGO 2 categories.
DBpedia uses the RESOURCE DESCRIPTION FRAMEWORK (RDF) to represent extracted information and consists of 3 billion RDF triples, of which 580 million were extracted from the English edition of Wikipedia and 2.46 billion from other language editions.


So Dbpedia dataset Useful Or Not?
The answer is - Yes it is very useful in natural language processing tasks.
Each and every dataset from DBpedia is potentially useful for several Natural Language Processing (NLP) tasks.
It has various number of datasets available -

1.Dbpedia Lexicalizations dataset -

Contains mappings between surface forms and URIs. A surface form is term that has been used to refer to an entity in text. Names and nicknames of people are examples of surface forms. We store the number of times a surface form was used to refer to a DBpedia resource in Wikipedia, and we compute statistics from that.

2.Dbpedia Topic signatures -

We tokenize all Wikipedia paragraphs linking to DBpedia resources and aggregate them in a Vector Space Model of terms weighted by their co-occurrence with the target resource. We use those vectors to select the strongest related terms and build topic signatures for those entities.
3.Dbpedia Thematic concepts -

Thematic Concepts are DBpedia resources that are the main subject of a Wikipedia Category.

4.Dbpedia people's grammatical gender -
Can be used for anaphora resolution and coreference resolution tasks.

References -



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