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Embodied cultural heritage information resources carry the bodily experiences and perceptual logic formed through human cultural practices, exhibiting high multimodality, immersiveness, and dynamic transmission characteristics. However, traditional methods of information organization struggle to reveal the tacit embodied knowledge within them. To address this, this study focuses on typical embodied cultural heritage information resources represented by the guqin jianzi notations, constructing an embodied semantic ontology model and proposing a bidirectional collaborative mechanism integrating large language model with ontology model to enhance the semantic understanding of embodied knowledge by large language models. The study constructed two embodied knowledge base, puzi knowledge base and jianzi knowledge base, and conducted multiple experiments to evaluate the performance of large language model in understanding the semantics of single jianzi and jianzi sequences. The results show that the embodied knowledge bases significantly enhance large language model’s ability to interpret complex cultural symbols, particularly improving accuracy in understanding embodied semantic dimensions such as fingering techniques, hui positions, and timbre. Theoretically, this study expands new pathways for semantic modeling of embodied cultural heritage; practically, it provides a feasible technical solution for the digital preservation and intelligent transmission of embodied cultural heritage.

This paper explores how users perceive and interact with AI in a crowdsourcing ancient texts digitization project. We conducted semi-structured interviews with 28 participants from the ancient text digitization crowdsourcing project “I am a Collator of Ancient Texts”. Participants viewed AI recognition as accurate and efficient but noted its limits with deteriorated text quality and complex layouts. We found that AI misidentification or failure to provide alternative character suggestions may reduce users’ task completion efficiency or even influence their task selection. A key theme emerging from the study is user trust development when interacting with AI—despite initial skepticism, trust in AI technologies gradually increased with positive interaction experiences, leading users to adjust their validation strategies. We also observed that participants acquired knowledge from AI recognition and suggestion results, but could also be misled when AI made errors. This study contributes to understanding human-AI collaboration in crowdsourced cultural heritage digitization and suggests that future platforms should provide customizable confidence indicators, clearer AI explanations, and learning supports to accommodate diverse users.

In recent years, cultural heritage institutions have experimented with opening up cultural heritage data and encouraging its reuse. While a growing body of digital humanities research has focused on the value of open data for cultural heritage and the development of data infrastructures, there is limited work exploring the searching behavior for open dataset in the cultural heritage domain at the micro level. This paper examines cultural heritage data curation competitions as a case to explore the open data searching behaviors of participants in such information practices. Using semi-structured interviews, field observations, and secondary data analysis, this study identifies participants' data needs, search strategies, evaluation criteria, and related challenges through open coding. The findings will help cultural heritage institutions to effectively organize and curate open datasets, and advocate for digital libraries to better integrate open data resources and build open data platforms.

Making visible LGBTQIA+ materials within cultural heritage institutions requires understanding the potentially positive and negative consequences of queer visibility. Deploying findings from interviews with 29 archivists, this paper explores how practitioners responsible for creating and managing finding aids for LGBTQIA+ archives navigate their work. This paper reveals how participants navigated clear understandings of the historical and theoretical implications rooted in naming queer identities within archival records. This paper places these configurations of archival description and queer visibility in conversation with both the reparative and anti-social lenses of queer theory. The first posits that expanding queerness in finding aids ensures a more queer-inclusive future, while the latter contends that inconsistencies between social support of queer individuals and the policies enacting such support remain too vast to ensure safety. The paper concludes with theoretical and praxis-based implications for this work in an era of automation and governmental backlash.