Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Joonhyung Bae

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.

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

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.

Paper Structure

This paper contains 53 sections, 2 equations, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Computational Analysis of Cultural and Institutional Ecosystems
  4. Cultural analytics.
  5. Domain-specific institutional mapping.
  6. Theoretical Foundations for Institutional Characterization
  7. Text Embeddings and Topic Modeling for Cultural Data
  8. Sentence-level embeddings.
  9. Topic modeling.
  10. Codebook quantization.
  11. Unsupervised Clustering and Interactive Visualization for Cultural Data
  12. Clustering and dimensionality reduction.
  13. Interactive visualization for cultural data.
  14. Research Gaps and Positioning
  15. Methodology
  16. ...and 38 more sections

Figures (7)

  • Figure 1: Overview of the ARTLAS processing pipeline. Qualitative axis descriptions for 78 institutions are encoded through E5-large-v2 sentence embeddings, quantized via a word-level codebook, and clustered using UMAP and agglomerative clustering. NMF topic modeling and entropy-based boundary analysis are applied post-clustering, and results are served through an interactive web visualization.
  • Figure 2: 2D UMAP scatter plots comparing Agglomerative Average ($k{=}10$, left) and DBSCAN ($k{=}2$, right). Agglomerative clustering yields 10 interpretable groups, while DBSCAN produces only two effective clusters with 27.5% of institutions classified as noise (gray).
  • Figure 3: Axis contribution analysis (leave-one-axis-out). Each bar represents the change in the respective metric when the named axis is removed.
  • Figure 4: 2D UMAP scatter plot of 78 institutions colored by cluster membership ($k{=}10$). (a) Full view showing the spatial separation of Cluster 4; (b) zoomed view of the nine remaining clusters with representative institution labels.
  • Figure 5: Cluster--topic heatmap. Each cell shows the mean NMF weight of the topic (column) within the cluster (row). Darker cells indicate stronger thematic associations.
  • ...and 2 more figures