Information Extraction (IE) tasks are commonly studied topics in various domains of research. Hence, the community continuously produces multiple techniques, solutions, and tools to perform such tasks. However, running those tools and integrating them within existing infrastructure requires time, expertise, and resources. One pertinent task here is triples extraction and linking, where structured triples are extracted from a text and aligned to an existing Knowledge Graph (KG). In this paper, we present PLUMBER, the first framework that allows users to manually and automatically create suitable IE pipelines from a community-created pool of tools to perform triple extraction and alignment on unstructured text. Our approach provides an interactive medium to alter the pipelines and perform IE tasks. A short video to show the working of the framework for different use-cases is available online under: https://www.youtube.com/watch?v=XC9rJNIUv8g
翻译:信息提取(IE)任务通常是研究不同研究领域的主题,因此,社区不断生成多种技术、解决方案和工具来完成这些任务。然而,将这些工具运行并融入现有基础设施需要时间、专门知识和资源。这里的一个相关任务是三重提取和连接,从文本中提取结构化的三重数据,并与现有的知识图(KG)相匹配。本文介绍PLUmbER,这是第一个允许用户手工和自动从社区创建的工具库中创建合适的 IE 管道,用于在无结构文本上进行三重提取和校准。我们的方法为改变管道和履行IE任务提供了一个互动的媒介。一个显示不同使用案例框架运作情况的短视频可在网上查阅:https://www.youtube.com/watch?v=XC9rJUv8g。