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Related Experiment Video

Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Investigating antiquities trafficking with generative pre-trained transformer (GPT)-3 enabled knowledge graphs: A

Shawn Graham1, Donna Yates2, Ahmed El-Roby3

  • 1Department of History, Carleton University, Ottawa, Ontario, Canada.

Open Research Europe
|August 30, 2023
PubMed
Summary

We used a large language model (GPT-3) to semi-automate knowledge graph creation for the antiquities trade, achieving comparable results to manual methods with significant time savings. This approach enhances the study of archaeological data.

Keywords:
Large language modelsantiquities tradeantiquities traffickingart marketgpt3illicit antiquitiesknowledge graphknowledge graph embedding model

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Area of Science:

  • Digital Humanities
  • Archaeology
  • Artificial Intelligence

Background:

  • The antiquities trade generates vast amounts of unstructured data across diverse sources like articles, auction catalogs, and archives.
  • Systematic examination of these sources is crucial for understanding the trade but is labor-intensive.
  • Knowledge graphs offer a structured way to represent complex relationships within this data.

Purpose of the Study:

  • To explore the efficacy of using a large language model (GPT-3) for semi-automating knowledge graph construction from scholarly texts on the antiquities trade.
  • To compare the quality and efficiency of AI-generated knowledge graphs against manually created ones.
  • To assess the potential of this method for discovering new insights into the antiquities trade.

Main Methods:

  • A prompt-guided GPT-3 was employed to extract subject-predicate-object relationships from articles, forming a knowledge graph.
  • The AI-generated knowledge graph was compared to a manually annotated version from the same source material.
  • Knowledge graphs were projected into a neural network embedding model (Ampligraph) to identify probable connections and relationships.

Main Results:

  • The semi-automatic knowledge graph generation using GPT-3 yielded results comparable to the manually created knowledge graph.
  • This AI-driven approach demonstrated substantial time savings compared to manual annotation.
  • The method allows for a potential expansion of the volume of materials that can be analyzed.

Conclusions:

  • Semi-automating knowledge graph creation with large language models is a viable and efficient method for analyzing the antiquities trade.
  • This computational approach has significant implications for processing archaeological knowledge found in grey literature and other scholarly formats.
  • The methodology offers a scalable solution for uncovering new research avenues in archaeology and related fields.