Related Experiment Video
Updated: Jul 17, 2025
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
276
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
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.
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.
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