Digital Prospecting: Semantic Engineering and Methodological Framework for Generative AI 《數位挖礦:生成式語義研究法的語義工程與方法論架構》

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Digital Prospecting: Semantic Engineering and Methodological Framework for Generative AI

This document presents “Digital Prospecting,” a methodological framework for generative-AI-based semantic research grounded in human–AI collaboration. The framework conceptualizes generative AI as a phenomenal field whose outputs may be systematically mined through five semantic strategies—anomalous, complementary, contrastive, echoic, and aggregative operations.

The approach highlights three principles:

• Generative AI functions not merely as a tool but as a semantic stratum available for exploration.

• Researchers can leverage model biases, associative structures, and automatic generation to extract reorganizable semantic patterns.

• The mining process is a form of semantic engineering, producing verifiable structures through iteration, comparison, and aggregation.

A case study based on AI-generated pseudo-characters demonstrates how anomalous mining reveals emergent morphological logic and cultural material. The proposed workflow applies to art, design, linguistics, and computational cultural studies, offering a systematic method for creators and researchers exploring AI’s generative space.

《數位挖礦:生成式語義研究法的語義工程與方法論架構》

本文件提出「數位挖礦」框架,一套以人機協作為核心的生成式語義研究方法論。該框架將生成式 AI 視為可觀測的「現象場」,並透過五項語義操作策略——異常、補集、對照、回聲、聚合——從統計模型的輸出中提取可重組的知識與創意模式。

方法論強調:

• 生成式 AI 不僅是工具,更是一種可供探勘的語義地層;

• 研究者可利用 AI 的偏誤、關聯網絡與自動生成能力,建立可驗證的語義結構;

• 挖礦行為本質上是一種「語義工程」,透過重複、對照與聚合實踐形成邏輯框架。

本方法論以〈AI 假字〉生成實作為案例,說明異常策略如何從模型誤差中發掘新的語形邏輯與文化材料。所提出的流程適用於藝術、設計、語言與計算文化研究等領域,旨在提供創作者與研究者探索生成空間的系統化工具。

作者背景 | Author Background

作者原職為台灣家具維修匠師與玩具設計師,無工程/研究訓練。2025年5月生成式AI成熟後,7個月內產出2篇DOI論文+20專案,方法源自工藝實作中的AI行為觀察。

Taiwan furniture craftsman & toy designer. No engineering/research training. May-Dec 2025: 2 DOIs + 20 proprietary cases. Methodology from hands-on AI observation in craft workflows.