This project started in 2021 and is looking to its 3rd participation in DBpedia’s GSoC.
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 (at the time of writing this) is semantically connected to 299 base entities.
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.
The repository where all source code will be stored is the following:
The goal of this project is to develop a framework for predicate resolution of wiki links among entities. During GSoC 2022, we employed a suite of machine-learning models to perform joint entity-relation extraction on open-domain text. However, after we extracted entities and relations from text, only some of these entities were disambiguated (i.e., mapped to a DBpedia entity) and none of these relations were disambiguated (i.e., mapped to a DBpedia property). Now we want to be able to link each node/entity and edge/relation to a resource in the DBpedia knowledge graph.
Last year we adopted a more general solution that targets multiple relationships at once, however the contributor may choose to extract complex relationships, such as:
- Causality. (Addressed during GSoC 2021, but not completed.) 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.
- Issuance. An abstract entity assigned to some agent, e.g., from the text
Messi won the award, his second consecutive Ballon d’Or victory.
- Any other direct relationship which is not found in DBpedia.
The contributor may use any Python deep learning framework and/or existing tool. The following resources are recommended for use in the project.
- The project repository linked above and the machine-learning models mentioned in the readme files found in each GSoC folder.
- Last year’s blog to understand the project status quo.
- Python Wikipedia makes it easy to access and parse data from Wikipedia.
- Huggingface Transformers for Natural Language Inference can be extremely useful to extract structured knowledge from text or perform zero-shot classification.
- DBpedia Lookup is a service available both online and offline (e.g., given a string, list all entities that may refer to it).
- DBpedia Anchor text is a dataset containing the text and the URL of all links in Wikipedia; the indexed dataset will be available to the student (e.g., given an entity, list all strings that point to it).
- An example of an excellent proposal that was accepted a few years ago.
The size of this project can be either medium or large. Please state in your proposal the number of total project hours you intend to dedicate to it (175 or 300).
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.
- Get familiar with SPARQL on the DBpedia endpoint.
- Run a local DBpedia Virtuoso endpoint.
- Understand the science behind relation extraction.
- Run and understand the pipeline implemented in the Jupyter notebook.