From Text to Network: Constructing a Knowledge Graph of Taiwan-Based China Studies Using Generative AI

TL;DR

This paper presents a novel AI-driven method to convert unstructured Taiwan-based China Studies literature into an interactive knowledge graph, facilitating new insights and navigation of the field's intellectual landscape.

Contribution

It introduces a generative AI approach to systematically extract and visualize entity relations from academic texts, creating a scalable, interactive knowledge infrastructure for regional studies.

Findings

Successfully extracted relation triples from 1,367 articles

Visualized a comprehensive knowledge graph of the field

Revealed new thematic clusters and research gaps

Abstract

Taiwanese China Studies (CS) has developed into a rich, interdisciplinary research field shaped by the unique geopolitical position and long standing academic engagement with Mainland China. This study responds to the growing need to systematically revisit and reorganize decades of Taiwan based CS scholarship by proposing an AI assisted approach that transforms unstructured academic texts into structured, interactive knowledge representations. We apply generative AI (GAI) techniques and large language models (LLMs) to extract and standardize entity relation triples from 1,367 peer reviewed CS articles published between 1996 and 2019. These triples are then visualized through a lightweight D3.js based system, forming the foundation of a domain specific knowledge graph and vector database for the field. This infrastructure allows users to explore conceptual nodes and semantic relationships across the corpus, revealing previously uncharted intellectual trajectories, thematic clusters, and research gaps. By decomposing textual content into graph structured knowledge units, our system enables a paradigm shift from linear text consumption to network based knowledge navigation. In doing so, it enhances scholarly access to CS literature while offering a scalable, data driven alternative to traditional ontology construction. This work not only demonstrates how generative AI can augment area studies and digital humanities but also highlights its potential to support a reimagined scholarly infrastructure for regional knowledge systems.