AI 中文总结
bikiDATA是一款为现代软件开发者设计的Python库，它抽象了RDF数据模型的复杂性，可用于查询和探索大规模RDF数据集，已在实际项目中应用并提升了相关性能。
AI 中文摘要
尽管知识图谱提供了无与伦比的数据灵活性，但RDF三元组与软件工程师使用的原生对象之间的语义差距仍是显著的入门障碍。开发知识图谱驱动的应用通常需要深厚的SPARQL专业知识和复杂的数据映射层。为降低这一门槛，我们提出bikiDATA：一种为现代软件开发者设计的高性能存储解决方案和Python库。与传统封装器不同，bikiDATA将RDF数据模型的复杂性抽象为开发者友好的API，适配Python生态系统的原生体验。除标准SPARQL支持外，该系统还为生产级应用提供全套功能，包括集成全文搜索、知识图谱嵌入和视觉相似性搜索。bikiDATA已在FIZ Karlsruhe的正在进行的项目中使用，它降低了集成复杂度，提升了可扩展性，并增强了查询性能。源代码和可执行演示笔记本可在此httpsURL获取。
英文摘要
While knowledge graphs offer unparalleled data flexibility, the semantic gap between RDF triples and the native objects used by software engineers remains a significant barrier to entry. Developing knowledge-graph-backed applications typically requires deep expertise in SPARQL and complex data-mapping layers. To lower this threshold, we present bikiDATA: a high-performance storage solution and a Python library engineered for the modern software developer. Unlike traditional wrappers, bikiDATA abstracts the complexities of the RDF data model into a developer-friendly API that feels native to the Python ecosystem. Beyond standard SPARQL support, the system provides a comprehensive suite for production-grade applications, including integrated full-text search, knowledge graph embeddings, and visual similarity search. Already in use in ongoing projects at FIZ Karlsruhe, bikiDATA reduces integration complexity, improves scalability, and enhances query performance. The source code and executable demo notebook are publicly available at https://github.com/ISE-FIZKarlsruhe/bikidata.
CommentsDemo paper accepted at 23rd European Semantic Web Conference (ESWC) May 10-14 2026 Dubrovnik, Croatia
