论文标题
GBUILDER：非结构化语料库的可扩展知识图构建系统
gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus
论文作者
论文摘要
我们设计了一个用户友好且可扩展的知识图构建（KGC）系统，用于从非结构化语料库中提取结构化知识。与现有的kgc系统不同，Gbuilder提供了灵活且用户定义的管道，以采用IE模型的快速开发。可以使用更多基于内置模板或启发式操作员和可编程操作员来适应来自不同域的数据。此外，我们还为Gbuilder设计了基于云的自适应任务计划，以确保其在大规模知识图构造上的可扩展性。实验评估证明了Gbuilder在统一平台中组织多个信息提取模型的能力，并确认其在大规模KGC任务上的高扩展性。
We design a user-friendly and scalable knowledge graph construction (KGC) system for extracting structured knowledge from the unstructured corpus. Different from existing KGC systems, gBuilder provides a flexible and user-defined pipeline to embrace the rapid development of IE models. More built-in template-based or heuristic operators and programmable operators are available for adapting to data from different domains. Furthermore, we also design a cloud-based self-adaptive task scheduling for gBuilder to ensure its scalability on large-scale knowledge graph construction. Experimental evaluation demonstrates the ability of gBuilder to organize multiple information extraction models for knowledge graph construction in a uniform platform, and confirms its high scalability on large-scale KGC tasks.
