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Introduction
Applied computational ontologies (ACOs) are standardized metadata vocabularies that provide rules to structure data through organizing and labeling, in a manner that can be understood by specialists and users working in different disciplines. ACOs can be found in products as diverse as virtual assistants like Siri, Cortana, Alexa and Bixby, in semantic web metadata on websites, apps and platforms, and in scientific research software and databases. In these areas, data exist in formats that are often incompatible and formalized locally. Data-labeling standards, for example, are made using general terms, are based on natural language or are adopted using formalized but limited classification systems. Such a lack of quality vocabularies for accessing and reasoning with heterogeneous data in uniform ways makes it hard to achieve the semantic interoperability of data across systems. Developed by researchers over the past three decades, one solution has been to provide logical (computable) definitions using controlled metadata vocabularies of preferred labels for describing data combined with tags – a practice known as applied ontology making.
Ontology engineering projects, in their quest to create semantically interoperable data entities, may be prone to several communication problems stemming from assumptions and biases in data reasoning. For example, ontology builders may disagree on shared terms or propose contradictory logics in the construction phase, or ontology users may mistakenly apply ontological principles. Since this is both a computational and a social process, analyzing such contextual ontology-making scenarios and communities requires the tools of qualitative inquiry and historical analysis along with digital methods. This paper is a first attempt at analyzing data-driven ontology practices through the framework of data assemblage theory (Kitchin, 2014; Kitchin and Lauriault, 2018) and proposes a multifaceted approach to studying applied ontology work. Building on qualitative work in knowledge representation and information infrastructure studies (Edwards et al., 2009, 2013; Plantin et al., 2018; Ribes and Bowker, 2009; Ribes and Finholt, 2009; Bowker et al., 2010) and research into data ethnography (Knox and Nafus, 2018), I present several years’ worth of research on an emerging, data-intensive form of scientific media that I refer to as ACO and offer several lenses through which to study the work of ACOs and their practitioners.
The paper’s last section contributes to knowledge through a...