This paper aims to examine how metadata taxonomies in embodied computing databases indicate context (e.g. a marketing context or an ethical context) and describe ways to…
This paper aims to examine how metadata taxonomies in embodied computing databases indicate context (e.g. a marketing context or an ethical context) and describe ways to track the evolution of the embodied computing industry over time through digital media archiving.
The authors compare the metadata taxonomies of two embodied computing databases by providing a narrative of their top-level categories. After identifying these categories, they describe how they structure the databases around specific themes.
The growing wearables market often hides complex sociotechnical tradeoffs. Marketing products like Vandrico Inc.’s Wearables Database frame wearables as business solutions without conveying information about the various concessions users make (about giving up their data, for example). Potential solutions to this problem include enhancing embodied computing literacy through the construction of databases that track media about embodied computing technologies using customized metadata categories. Databases such as FABRIC contain multimedia related to the emerging embodied computing market – including patents, interviews, promotional videos and news articles – and can be archived through user-curated collections and tagged according to specific themes (privacy, policing, labor, etc.). One of the benefits of this approach is that users can use the rich metadata fields to search for terms and create curated collections that focus on tradeoffs related to embodied computing technologies.
This paper describes the importance of metadata for framing the orientation of embodied computing databases and describes one of the first attempts to comprehensively track the evolution of embodied computing technologies, their developers and their diverse applications in various social contexts through media archiving.
Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The…
Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.
The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.
The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.
The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.