Creating and Querying Personalized Versions of Wikidata on a Laptop

TL;DR

This paper introduces KGTK Kypher, a new query language and processor that enables users to create personalized, full-scale Wikidata variants on a laptop, supporting complex queries faster than traditional methods.

Contribution

The paper presents KGTK Kypher, a novel query system that allows efficient, personalized querying of the entire Wikidata knowledge graph on a laptop.

Findings

Kypher queries run significantly faster than SPARQL on large datasets.

Users can perform complex analyses on full Wikidata locally.

Kypher enables integration of external data sources like DBpedia.

Abstract

Application developers today have three choices for exploiting the knowledge present in Wikidata: they can download the Wikidata dumps in JSON or RDF format, they can use the Wikidata API to get data about individual entities, or they can use the Wikidata SPARQL endpoint. None of these methods can support complex, yet common, query use cases, such as retrieval of large amounts of data or aggregations over large fractions of Wikidata. This paper introduces KGTK Kypher, a query language and processor that allows users to create personalized variants of Wikidata on a laptop. We present several use cases that illustrate the types of analyses that Kypher enables users to run on the full Wikidata KG on a laptop, combining data from external resources such as DBpedia. The Kypher queries for these use cases run much faster on a laptop than the equivalent SPARQL queries on a Wikidata clone running on a powerful server with 24h time-out limits.