GPTKB 2.0: Direct Construction of Disambiguated Knowledge Bases from Large Language Models
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Computer Science > Computation and Language
Title:GPTKB 2.0: Direct Construction of Disambiguated Knowledge Bases from Large Language Models
Abstract:Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large language models (LLMs), treating the model itself as the knowledge source. However, LLMs natively possess no representation of entities, leading to duplicate entries as well as conflations. We propose GPTKB 2.0, a methodology for constructing disambiguated KBs directly from LLMs. GPTKB 2.0 incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy. We analyze the central design decisions and characterize the trade-offs between accuracy, scale, and cost. We execute GPTKB 2.0 at scale, obtaining a materialized KB containing over 1M disambiguated entities and 38.4M triples. This represents the first million-scale LLM-native KB with explicit internal canonicalization of entities, relations, and classes, a significant departure from prior Wikimedia-centric works. GPTKB 2.0 is available at this https URL.
| Comments: | 19 pages, 4 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB) |
| Cite as: | arXiv:2608.03729 [cs.CL] |
| (or arXiv:2608.03729v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.03729
arXiv-issued DOI via DataCite (pending registration)
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