ARTLAS: Mapping Art-Technology Institutions via Conceptual Axes, Text Embeddings, and Unsupervised Clustering

2026-03-28

AI Summary

This study addresses the current lack of a systematic framework for analyzing the multidimensional characteristics of increasingly diverse art–technology institutions. The authors propose an eight-dimensional conceptual framework that integrates data-driven methods—including E5-large-v2 text embeddings, TF-IDF word-level codebooks, UMAP dimensionality reduction, and hierarchical clustering—to uniformly map and perform unsupervised clustering on 78 institutions. By synthesizing qualitative dimensions with quantitative analysis, the approach achieves high clustering quality (silhouette coefficient: 0.803), revealing four distinct institutional clusters: art-science, industrial innovation, academic conferences, and electronic music, as well as bridging institutions that transcend thematic boundaries. An interactive visualization tool is also developed, offering a reproducible and scalable analytical paradigm for future research.

Abstract

The global landscape of art-technology institutions, including festivals, biennials, research labs, conferences, and hybrid organizations, has grown increasingly diverse, yet systematic frameworks for analyzing their multidimensional characteristics remain scarce. This paper proposes ARTLAS, a computational methodology combining an eight-axis conceptual framework (Curatorial Philosophy, Territorial Relation, Knowledge Production Mode, Institutional Genealogy, Temporal Orientation, Ecosystem Function, Audience Relation, and Disciplinary Positioning) with a text-embedding and clustering pipeline to map 78 cultural-technology institutions into a unified analytical space. Each institution is characterized through qualitative descriptions along the eight axes, encoded via E5-large-v2 sentence embeddings and quantized through a word-level codebook into TF-IDF feature vectors. Dimensionality reduction using UMAP, followed by agglomerative clustering (Average linkage, k=10), yields a composite score of 0.825, a silhouette coefficient of 0.803, and a Calinski-Harabasz index of 11,196. Non-negative matrix factorization extracts ten latent topics, and a neighbor-cluster entropy measure identifies boundary institutions bridging multiple thematic communities. An interactive web-based visualization tool built with React enables stakeholders to explore institutional similarities, thematic profiles, and cross-disciplinary connections. The results reveal coherent groupings such as an art-science hub cluster anchored by ZKM and ArtScience Museum, an innovation and industry cluster including Ars Electronica, transmediale, and Sonar, an ACM academic community cluster comprising TEI, DIS, and NIME, and an electronic music and media cluster including CTM Festival, MUTEK, and Sonic Acts. This work contributes a replicable, data-driven approach to institutional ecology in the cultural-technology sector.

Problem

• art-technology institutions
• institutional analysis
• multidimensional characteristics
• systematic framework
• cultural-technology sector

Innovation

• text embeddings
• unsupervised clustering
• institutional mapping
• conceptual axes
• interactive visualization