Computational curation of traditional martial arts: from archives to systems

Abstract Intangible Cultural Heritage (ICH) poses distinct challenges for documentation and digital representation, as it is expressed through embodied practices and ideologies shaped by complex histories. Recent advances in digital tools have enabled richer modes of recording and opened new possibilities for studying how ICH knowledge may be preserved, experienced, and transmitted. Addressing these opportunities requires methodologies that reconcile the dynamic nature of living practices with the constraints of traditional archival systems, particularly in relation to accessing and analysing knowledge embedded in archival data. This work responds by proposing a systems thinking approach applied to traditional martial arts (TMA), implemented through computational experimentation that synthesizes three critical aspects: encoding embodied practice within its contextual meanings; translating humanities scholarship into viable representational models; and devising interaction strategies to facilitate engagement with the encoded knowledge. Drawing on the strengths and gaps of the Hong Kong Martial Arts Living Archive with respect to knowledge transmission, this research combines theoretical inquiry with empirical studies to examine the multifaceted episteme of TMA, which informs the development of the Martial Arts Ontology (MAon), formalizing key TMA concepts. The coupling of MAon-based annotation with deep-learned motion embeddings encodes TMA knowledge from multimodal materials into operable data that carry the semantics of texts, acts, and minds. These encodings support the development of an interactive knowledge system for informative and exploratory learning, while the process informs a computational curation pipeline in which interdisciplinary researchers act as translators and operators, mediating the culturally meaningful datafication of embodied knowledge archives.