Unlocking a multimodal archive of Southern Chinese martial arts through embodied cues

• Published in: Journal of documentation 2024-09, Vol.80 (5), p.1148-1166
• Main Authors: Hou, Yumeng, Seydou, Fadel Mamar, Kenderdine, Sarah
• Format: Article
• Language: English
• Subjects: Algorithms; Archival description; Archives; Archives & records; Artificial intelligence; Cultural Background; Cultural heritage; Cultural resources; Cultural Traits; Dance; Dance notation; Deep learning; Documentation; Educational Objectives; Ethnic Groups; Human body; Human mechanics; Intellectual Property; Inventions; Knowledge; Knowledge representation; Machine learning; Martial arts; Minority & ethnic groups; Motion; Motion capture; Multimedia; Multimedia Materials; Retrieval; Science Instruction; Semantics; Workflow
• ISSN: 0022-0418; 1758-7379
• DOI: 10.1108/JD-01-2022-0027

PurposeDespite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.Design/methodology/approachThis research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.FindingsThrough experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.Originality/valueThis work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.