ORCA：数据Web爬虫的基准测试
ORCA: a Benchmark for Data Web Crawlers
摘要：
Web上可用的RDF知识图谱的数量不断增长。因此，为下游应用程序大规模收集这些图需要使用爬虫。虽然存在数据Web爬虫，并且可以对一般Web爬虫进行调整以专注于数据Web，但目前还没有基准来公平地评估它们的性能。我们的工作通过提出Orca基准来缩小这一差距。Orca生成一个合成数据Web，它与原始Web解耦，并支持对数据Web爬虫进行公平和可重复的比较。我们的评估表明，Orca可以用来揭示现有爬虫的不同优点和缺点。该基准测试是开源的，可在https://github.com/dice-group/orca上获得。
英文摘要：
The number of RDF knowledge graphs available on the Web grows constantly. Gathering these graphs at large scale for downstream applications hence requires the use of crawlers. Although Data Web crawlers exist, and general Web crawlers could be adapted to focus on the Data Web, there is currently no benchmark to fairly evaluate their performance. Our work closes this gap by presenting the Orca benchmark. Orca generates a synthetic Data Web, which is decoupled from the original Web and enables a fair and repeatable comparison of Data Web crawlers. Our evaluations show that Orca can be used to reveal the different advantages and disadvantages of existing crawlers. The benchmark is open-source and available at https://github.com/dice-group/orca.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
