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

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.

KEYWORDS:

• Search engines
• knowledge
• linked data
• semantic search
• Semantic Web
• sociology of technology

Disclosure statement

No potential conflict of interest was reported by the author.

ORCID

Vivienne Waller http://orcid.org/0000-0002-5353-9304

Notes

1. For example, in a presentation on semantic search, Peter Mika, Director of Semantic Search at Yahoo, shows information that is supposed to be on Michaelangelo, the Italian Renaissance artist, displaying a picture of Michaelangelo the Ninja Turtle, and information on the rapper Ice Cube, describing him as a small cube of frozen water (‘Understanding queries through entities’, Slideshare Presentation, 7 November 2014. http://www.slideshare.net/pmika).

2. See (Kallinikos 2006) for a discussion of the continuum between data, information and knowledge.

3. http://www.theguardian.com/technology/2014/nov/03/larry-page-google-dont-be-evil-sergey-brin.

4. Accessed May 19, 2015. www.wolframalpha.com.

5. https://www.apple.com/au/ios/siri/.

6. Throughout the rest of this paper, I use the term ‘computer scientists’ as shorthand to refer to those mathematicians, engineers and computer scientists involved in the design and development of the Semantic Web.

7. Accessed October 2, 2015. http://www.w3.org/standards/.

8. Accessed June 7, 2015. www.schema.org.

9. Just as semantic search involves the reduction of knowledge to render it palatable to machines, inevitably, there is reduction involved in this explanation of semantic search in order for it to be palatable to non-computer scientists. The irony of this endeavour is not lost on the author.

10. http://www.hon.ch/Global/copyright.html.

11. http://www.w3.org/standards/semanticweb/ontology.

12. Accessed October 2, 2015. http://www.nlm.nih.gov/pubs/factsheets/umls.html.

13. See http://www.w3.org/2001/sw/sweo/public/UseCases/ for a wide range of real-world examples of its use.

14. Accessed May 29, 2015. http://wiki.dbpedia.org/about.

15. Accessed May 29, 2015. wiki.dbpedia.org/about.

16. Until 2012, Bing reference labelled itself as ‘the decision engine’.

17. I attended a talk by Etienne Wenger in the early 2000s where he gave the example of how it was only through participation in a wine group with numerous tastings that he gradually learnt the meaning of the wine connoisseur concept of ‘purple on the nose’.

18. Accessed October 2, 2015. www.internetlivestats.com/google-search-statistics/.