Towards a Neural Extraction Framework

Background

Every Wikipedia article links to a number of other articles. In DBpedia, we keep track of these links through the dbo:wikiPageWikiLink property. Thanks to them, we know that the :Berlin_Wall entity is semantically connected to 299 base entities.

This project is funded by Google Summer of Code 2022.

Problem

However, only 9 out of 299 base entities are linked from :Berlin_Wall via also another predicate. This suggests that in the large majority of cases, it is not clear what kind of relationship exists between the entities. In other words, DBpedia does not know what specific RDF predicate links the subject (in our case, :Berlin_Wall) to any of the objects above. Currently, such relationships are extracted from tables and the infobox (usually found top right of a Wikipedia article) via the Extraction Framework. Instead of extracting RDF triples from semi-structured data only, we want to leverage information found in the entirety of a Wikipedia article, including page text.

Goals

The goal of this project is to develop a framework for predicate resolution of wiki links among entities. I choose to focus on a specific kind of relationship:

• Causality. The direct cause-effect between events, e.g., from the text

  The Peaceful Revolution (German: Friedliche Revolution) was the process of sociopolitical change that led to the opening of East Germany’s borders with the west, the end of the Socialist Unity Party of Germany (SED) in the German Democratic Republic (GDR or East Germany) and the transition to a parliamentary democracy, which enabled the reunification of Germany in October 1990.

Expected result: :Peaceful_Revolution –––dbo:effect––> :German_reunification

• Issuance. An abstract entity assigned to some agent, e.g., from the text

  Messi won the award, his second consecutive Ballon d’Or victory.

Expected result: :2010_FIFA_Ballon_d’Or –––dbo:recipient––> :Lionel_Messi

• Any other direct relationship which is not found in DBpedia.
• A more general solution that targets multiple relationships at once.

Impact

This project will potentially generate millions of new statements. This new information could be released by DBpedia to the public as part of a new dataset. The creation of a neural extraction framework could introduce the use of robust parsers for a more accurate extraction of Wikipedia content.

The open-source codes

This project started in 2021 and is currently in its 2nd iteration in GSoC.

Mentors

Tommaso Soru, Diego Moussallem, Ziwei Xu