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DBpedia's GSoC warmup tasks
Tommaso Soru edited this page May 5, 2020
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Hi! If you landed on this page, you are likely interested in becoming a GSoC student for DBpedia. In the following, the warmup tasks are described in details.
- Read the papers:
- Train a Neural SPARQL Machine model on a DBpedia class of your choice.
- In this example, we are going to select
dbo:Monument
as the class of instances to perform QA on. - In the template file
data/annotations_monument.csv
, you may find a collection of query templates. The columns show the following values:- Column 1: target class of the first placeholder
<A>
in a question (e.g.,dbo:Monument
) - Column 2: empty for special use
- Column 3: target class of the second placeholder
<B>
in a question, if it exists (e.g.,dbo:Monument
) - Column 4: question template in natural language (e.g.,
is <A> more recent than <B>
) - Column 5: SPARQL query template which translates column 4 (e.g.,
ask where { <A> dbp:complete ?a . <B> dbp:complete ?b . FILTER(?a > ?b) }
) - Column 6: SPARQL generator query to collect examples for placeholders
<A>
,<B>
, etc. (e.g.,select distinct(?x) ?y where { ?x dbp:complete [] . ?y dbp:complete [] . FILTER(?x != ?y) }
)
- Column 1: target class of the first placeholder
- Create a template file for a class of your choice with at least 20 query templates. Hints:
- Mind the correct spacing in the SPARQL query.
- Use all-lowercase letters in questions except for placeholders.
- To speed up experiments, choose one having less than 1,000 instances.
- Follow the data preparation, training, and inference sections in the readme file.
- In this example, we are going to select
- Share your results! We are curious how well NSpM perform on each use-case.