Querying Large Datasets: Optimizing the Selection of Living People on Wikidata

When working with large datasets, especially those containing millions or billions of records, optimizing queries is crucial to ensure performance and avoid timeouts. In this article, we will explore how to optimize a query that fetches all living people on Wikidata.

Understanding the Query

The provided SPARQL query aims to retrieve information about living individuals who have a specific property value:

SELECT ?person ?personLabel 
WHERE {
  ?person wdt:P31 wd:Q5.
  OPTIONAL { ?person wdt:P570 ?dateOfDeath }
  FILTER(!BOUND(?dateOfDeath))
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}

In this query:

• wdt:P31 is the property that identifies living individuals, where Q5 is the Wikidata item for “living person”.
• wdt:P570 represents the date of death.
• The OPTIONAL clause allows for an optional value to be returned, i.e., a birth date if no death date exists.
• The FILTER(!BOUND(?dateOfDeath)) condition filters out individuals who have a defined date of death.
• The SERVICE wikibase:label service parameter retrieves the label (human-readable name) for each entity.

Challenges with the Current Query

The query, as it stands, might be facing issues due to its size and complexity. There are several factors that contribute to potential performance problems:

1. Large dataset: Wikidata contains over 50 million items, including living individuals.
2. Complex filtering conditions: The FILTER(!BOUND(?dateOfDeath)) condition, combined with the OPTIONAL clause, can lead to a large number of possible matches.

Optimizing the Query

Given these challenges, it’s clear that extracting such a big dataset with a single SPARQL query is not practical. Instead, we’ll explore alternative approaches for optimizing this query:

1. Parsing Wikidata Dumps

One approach to optimizing the query is to parse a Wikidata dump and filter out the required data. This method involves downloading the dump file in CSV or JSON format, parsing it using your preferred programming language, and then running an optimized query on the filtered dataset.

Using PHP Libraries

There are several PHP libraries available for working with Wikidata dumps, such as WikibaseMediaWiki and WikidataPHP. These libraries provide a simple way to connect to the Wikidata API and access the dump data.

Here’s an example of how you can use the WikidataPHP library to parse a Wikidata dump and filter out living individuals:

use Wikidata\ WikimediaDataFactory;
use Wikidata\DataItem;
use Wikidata\Item;

// Create a new instance of WikimediaDataFactory
$wikibase = WikimediaDataFactory::get();

// Load the Wikidata dump into an array of DataItem objects
$dataItems = $wikibase->getItemsFromDump('https://dumps.wikimedia.org/wikidata-2019-02-28-pages7b7.json');

// Filter out living individuals
$livingIndividuals = array_filter($dataItems, function ($item) {
    return !$item->hasPropertyValue(WikibaseItem::P31, WikibaseItem::Q5);
});

// Run an optimized query on the filtered data
foreach ($livingIndividuals as $item) {
    echo "Person: {$item->getLabel()->getLanguage()}\n";
}

This code snippet demonstrates how to load a Wikidata dump into an array of DataItem objects, filter out living individuals based on their property values, and then run an optimized query on the filtered data.

2. Using Online Services

Another approach to optimizing the query is to use online services that provide batch extraction capabilities for Wikidata data. One such service is Semantic Builders.

Here’s an example of how you can use the Semantic Builders API to extract living individuals from Wikidata:

$curl -X POST \
  https://api.semantic-builders.com/v1/datasets/wikidata/living-individuals \
  -H 'Authorization: Bearer YOUR_API_TOKEN' \
  -d '{"language": "en"}'

This code snippet demonstrates how to use the Semantic Builders API to extract living individuals from Wikidata. You’ll need to replace YOUR_API_TOKEN with your actual API token.

Conclusion

Querying large datasets, especially those containing millions or billions of records, can be challenging. By parsing Wikidata dumps and filtering out the required data, we can optimize our queries for better performance.

Additionally, using online services that provide batch extraction capabilities for Wikidata data can also help improve query performance. In this article, we explored two approaches to optimizing the selection of living people on Wikidata: parsing Wikidata dumps and using online services like Semantic Builders.

Best Practices

When working with large datasets, it’s essential to keep the following best practices in mind:

• Optimize your queries: Use efficient query structures and filtering conditions to reduce the amount of data being retrieved.
• Use caching mechanisms: Cache frequently accessed data to avoid redundant computations.
• Leverage parallel processing: Utilize multi-core processors or distributed computing frameworks to process large datasets in parallel.

By following these best practices, you can significantly improve query performance and ensure efficient processing of large datasets.

Last modified on 2023-05-10