🤖 AI Summary
To address the challenge of systematically integrating and discovering knowledge from vast volumes of unstructured academic papers in China Studies (CS) within Taiwan, this study proposes a generative AI–driven knowledge graph construction paradigm, replacing traditional ontology engineering. Leveraging 1,367 scholarly papers published between 1996 and 2019, we integrate large language model–based triple extraction, entity normalization and mapping, vector database–enabled semantic retrieval, and D3.js–powered interactive visualization to construct the first dynamic domain-specific knowledge graph for Taiwan’s CS field. The resulting graph supports interactive concept-node querying, semantic relationship tracing, and thematic clustering analysis. It effectively uncovers latent scholarly trajectories, emerging research fronts, and structural knowledge gaps, thereby significantly enhancing cross-disciplinary literature discovery efficiency and analytical insight generation.
📝 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.