一句话：提出 IoTutorMine，用视频转录与零样本大语言模型从 IoT 教程视频中提取结构化硬件物料清单，并发布基准与 Web 原型。
物联网教程视频常以口述而非画面说明需购买的硬件组件，使面向编程录屏的基于帧挖掘方法难以识别。本文提出 IoTutorMine，结合 YouTube 自动转录与零样本大语言模型，从教程视频中恢复结构化硬件物料清单（BoM），并采用句法匹配与语义规范化两级实体解析。系统提供 Web 原型，支持按需生成 BoM 或按组件检索教程库。作者发布含 20 个手工标注教程的基准，覆盖 Arduino 与 Raspberry Pi，含 131 个真值组件、别名词典与评估脚本。Gemini 3 Flash、GPT-5.5、Claude Opus 4.7 的 F1 分别为 0.933、0.887、0.868，Gemini 逐视频 F1 显著优于 Claude（p=0.028）。结果表明仅用转录提取 BoM 可行，语义映射对处理硬件命名差异至关重要。
Internet of Things (IoT) tutorial videos describe the parts a learner must purchase verbally rather than visually, which makes them invisible to frame-based mining techniques developed for programming screencasts. We present IoTutorMine, a tool that recovers structured hardware Bills of Materials (BoMs) from IoT tutorial videos by combining YouTube’s auto-generated transcripts with large language models in a zero-shot setting. IoTutorMine-Web, a public prototype, exposes the pipeline as an interactive IoT tutorial catalog: users paste a YouTube URL to get an on-demand BoM, or browse a pre-indexed library searchable by component (e.g., “every tutorial that uses a DHT11”). We also release a benchmark of 20 manually annotated YouTube tutorials covering Arduino and Raspberry Pi at two difficulty levels, with 131 ground-truth components from 16 distinct creators, together with an expert-curated alias dictionary and evaluation scripts. Under a two-tier entity-resolution strategy that combines syntactic matching with semantic canonicalization, Gemini 3 Flash reaches F1 = 0.933, GPT-5.5 F1 = 0.887, and Claude Opus 4.7 F1 = 0.868, with Gemini significantly outperforming Claude on per-video F1 (Wilcoxon p = 0.028). The results demonstrate that transcript-only BoM extraction is feasible for practical IoT cataloging applications, and that a semantic mapping layer is essential to resolve hardware nomenclature variations.
@inproceedings{paperbot12219,
title = {IoTutorMine: A Tool for Mining Hardware Bills of Materials from IoT Tutorial Videos},
author = {Abdullah A. Alahmadi and Ahmed O. Bahaj and Mohammad D. Alahmadi},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834647}
}