Bioart brings living material into artistic practice, where a single work can be at once an aesthetic object, a scientific instrument, and an ethical provocation. Traditional categories sort such works along one axis at a time, which flattens the very hybridity that defines the field and leaves curators no way to compare works across many dimensions together. I introduce BioArtlas, a computational atlas that represents each bioartwork along many curated dimensions at once and organizes the field by conceptual similarity rather than by medium or chronology. My method embeds the keywords of all 81 works on each of thirteen interpretive axes, groups related concepts into a shared codebook that tames inconsistent terminology, and then searches systematically for a clustering that is both statistically clean and interpretable. Among the methods that place every work on the map, agglomerative clustering separates the field far more cleanly than the usual k-means baseline (silhouette 0.664 versus 0.483), whereas density-based methods reach higher scores only by discarding most of the corpus as noise. By separating rigorous analysis from public storytelling, BioArtlas turns the tangled complexity of bioart into a navigable landscape, openly available as an interactive interface (https://www.bioartlas.com) and dataset (https://github.com/joonhyungbae/BioArtlas).

翻译：生物艺术将生命材料引入艺术实践，使单个作品同时成为审美对象、科学仪器和伦理挑战。传统分类方式仅沿单一维度对这类作品进行划分，这抹平了定义该领域本质的混合性，也使策展人无法跨多个维度对作品进行整体比较。我提出BioArtlas——一个计算图谱，它沿多个策展维度同时表征每件生物艺术作品，并按概念相似性而非媒介或时间顺序组织该领域。该方法将全部81件作品的关键词嵌入十三个解释性轴线上，将相关概念归入一个共享码本以规范不一致的术语，随后系统性地搜索兼具统计清晰性与可解释性的聚类方案。在所有将每件作品映射到图谱的方法中，凝聚聚类对该领域的划分效果远优于常规的k-means基线（轮廓系数：0.664 vs 0.483），而基于密度的方法要达到更高分数，则需将大部分语料作为噪声丢弃。通过将严谨分析与公开叙述相分离，BioArtlas将生物艺术错综复杂的复杂性转化为可导航的景观，并以交互界面（https://www.bioartlas.com）和数据集（https://github.com/joonhyungbae/BioArtlas）的形式开放共享。