The Wikidata Query Logs Dataset

2026-02-16

AI Summary

This work addresses the limitation of existing Wikidata question-answering datasets, which predominantly rely on template-based generation and fail to capture the complexity of real user queries. To overcome this, the authors propose a novel agent-driven iterative framework that leverages authentic SPARQL query logs from the Wikidata Query Service. Through a pipeline involving de-anonymization, cleaning, semantic validation, and natural language generation, the framework automatically constructs high-quality question-answer pairs without dependence on predefined templates, thereby significantly enhancing data authenticity and diversity. The resulting dataset, WDQL, comprises 200,000 question-answer pairs—more than six times larger than current comparable resources—and demonstrates substantial improvements in downstream question-answering performance. All data and code are publicly released to support further research.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Community question answering

Abstract

We present the Wikidata Query Logs (WDQL) dataset, a dataset consisting of 200k question-query pairs over the Wikidata knowledge graph. It is over 6x 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 dataset's benefit for training question-answering methods. All WDQL assets, as well as the agent code, are publicly available under a permissive license.

Problem

• Wikidata
• question-answering
• query logs
• SPARQL
• knowledge graph

Innovation

• Wikidata Query Logs
• agent-based query cleaning
• real-world SPARQL queries
• question-query alignment
• knowledge graph QA