DATA-DRIVEN ONTOLOGY PRACTICE

Abstract

Data exist in formats that are often incompatible and formalized only locally—data-labelling standards 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 much harder to achieve semantic interoperability of data across systems. Data can be spread over multiple locations and databases that often lack consistency, remaining unstructured and piled in data silos that are unable to interact. Developed by researchers over the past three decades, one solution has been to provide logical (computable) definitions using controlled vocabularies of preferred labels for describing data combined with tags—a practice known as ontology-making. The papers in this panel address several shared problems related to data-driven ontology practice and propose multi-faceted approaches to ontology work. Questions addressed by this panel include: Is there evidence that ontologies typify logics or biases? What types of data do ontologies organize? How are ontologies practically applied in scientific and social contexts? What does ontology work look like in research projects at multiple scales? How does the Internet help solve or problematize issues in ontology? What methodologies are currently deployed by ontologists and Internet researchers? These questions inform the papers on this panel and represent some of the emerging ontological concerns in Internet-based research and practice. Building on previous work in critical data studies, the papers presented in this panel treat ontology as the intersection of information and materiality.