Dear DBpedia community,
I'm Wojtek and hereby I would like to introduce myself as this year's
Google Summer of Code student.
My contribution to DBpedia will be mining topic models and extending
DBpedia Spotlight's functionality by predicting the topics of the annotated
document.
Attached you can find the abstract of my proposal.
Best regards,
Wojtek Lukasiewicz
*Abstract*:
DBpedia, a crowd- and open-sourced community project extracting the content
from Wikipedia, stores this information in a huge RDF graph. DBpedia
Spotlight is a tool which delivers the DBpedia resources that are being
mentioned in the document.
Using DBpedia Spotlight to extract and disambiguate Named Entities from
Wikipedia articles and then applying a topic modelling algorithm (e.g. LDA)
with URIs of DBpedia resources as features would result in a model, which
is capable of describing the documents with the proportions of the topics
covering them. But because the topics are also represented by DBpedia URIs,
this approach could result in a novel RDF hierarchy and ontology with
insights for further analysis of the emerged subgraphs.
The direct implication and first application scenario for this project
would be utilizing the inference engine in DBpedia Spotlight, as an
additional step after the document has been annotated and predicting its
topic coverage.
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