构建了33.5万条真实SPARQL查询与问题对，用于训练问答系统。
The Wikidata Query Logs Dataset
- 用真实查询日志构建数据集，非模板生成。
- 将匿名查询还原为可运行的语义查询，准确率超90%。
- 适合研究知识图谱问答与自然语言生成的学者使用。
我们提出了Wikidata查询日志（WDQL）数据集，包含33.5万条基于Wikidata知识图谱的真实SPARQL查询与对应自然语言问题对，规模超过现有同类数据集11倍以上，且不依赖模板生成。数据源自真实用户提交至Wikidata查询服务的请求，经匿名处理后需重新解析方可使用。为此，我们提出一种基于智能体的方法，通过迭代去匿名化、清洗和验证，将原始日志恢复为有效查询，并生成相应问题。实验表明该数据集在训练问答模型方面具有显著优势。所有数据及代码已开源，可通过https://github.com/ad-freiburg/wikidata-query-logs 获取。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。