Solving Guesstimation Problems Using the Semantic Web: Four Lessons from an Application

Alan Bundy, Gintautas Sasnauskas, Michael Chan

Research output: Contribution to journalArticlepeer-review


We draw on our experience of implementing a semi-automated guesstimation application of the Semantic Web, gort, to draw four lessons, which we claim are of general applicability. These are:
1. Inference can unleash the Semantic Web: The full power of the web will only be realised when we can use it to infer new knowledge from old.
2. The Semantic Web does not constrain the inference mechanisms: Since we must anyway curate the knowledge we extract from the web, we can take the opportunity to translate it into what ever representational formalism is most appropriate for our application. This also enables the use of whatever inference mechanism is most appropriate.
3. Curation must be dynamic: Static curation is not only infeasible due to the size and
growth rate of the Semantic Web, but curation must be application-specific.
4. Own up to uncertainty: Since the Semantic Web is, by design, uncontrolled, the accuracy of knowledge extracted from it cannot be guaranteed. The resulting uncertainty must not be hidden from the user, but must be made manifest.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalSemantic Web
Early online date10 Oct 2013
Publication statusPublished - 10 Oct 2013


  • semantic Web
  • Guesstimation


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