The Wikidata Query Logs Dataset

TL;DR

The paper introduces the Wikidata Query Logs dataset, comprising 335,000 question-query pairs derived from real-world SPARQL queries, enabling improved training of question-answering systems.

Contribution

It presents a novel large-scale dataset constructed from actual query logs and an agent-based method for de-anonymizing and verifying queries, which was not previously available.

Findings

Dataset is over 11 times larger than existing similar datasets.

The agent-based method effectively de-anonymizes and verifies queries.

The dataset improves training for question-answering methods.

Abstract

We present the Wikidata Query Logs (WDQL) dataset, a dataset consisting of 335k question-query pairs over the Wikidata knowledge graph. It is over 11x larger than the largest existing Wikidata datasets of similar format without relying on template-generated queries. Instead, we construct it using real-world SPARQL queries sent to the Wikidata Query Service and generate questions for them. Since these log-based queries are anonymized, and therefore often do not produce results, a significant amount of effort is needed to convert them back into meaningful SPARQL queries. To achieve this, we present an agent-based method that iteratively de-anonymizes, cleans, and verifies queries against Wikidata while also generating corresponding natural-language questions. We demonstrate the benefit of this dataset for training question-answering methods. All WDQL assets, as well as the agent code, are publicly available via https://github.com/ad-freiburg/wikidata-query-logs under a permissive license.