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gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus

2022-08-20 15:07:06
Yanzeng Li, Lei Zou
arXiv_CL
         
Abstract

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 embracing 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 not only demonstrates the ability of gBuilder to organize multiple information extraction models for knowledge graph construction in a uniform platform, and also confirms its high scalability on large-scale KGC task.

Abstract (translated)

URL

https://arxiv.org/abs/2208.09705

PDF

https://arxiv.org/pdf/2208.09705.pdf

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