gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 123 references that are in the map. First posted on arXiv 20 Aug 2022.
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26 of 123 references.
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.