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Making knowledge machine-processable: some implications of general semantic search

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  • Vivienne Waller

Abstract

The central argument of this paper is that the design, implementation and use of technologies that underpin general semantic search have implications for what we know and the way in which knowledge is understood. Semantic search is an assemblage of technologies that most Internet users would use regularly without necessarily realising. Users of search engines implementing semantic search can obtain answers to questions rather than just retrieve pages that include their search query. This paper critically examines the design of the Semantic Web, upon which semantic search is based. It demonstrates that implicit in the design of the Semantic Web are particular assumptions about the nature of classification and the nature of knowledge. The Semantic Web was intended for interoperability within specific domains. It is here argued that the extension to general semantic search, for use by the general public, has implications for what type of knowledge is visible and what counts as legitimate knowledge. The provision of a definitive answer to a query, via the reduction of discursive knowledge into machine-processable data, provides the illusion of objectivity and authority in a way that is increasingly impenetrable to critical scrutiny.

Suggested Citation

  • Vivienne Waller, 2016. "Making knowledge machine-processable: some implications of general semantic search," Behaviour and Information Technology, Taylor & Francis Journals, vol. 35(10), pages 784-795, October.
  • Handle: RePEc:taf:tbitxx:v:35:y:2016:i:10:p:784-795
    DOI: 10.1080/0144929X.2016.1183710
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