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11.3: Exercises

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    Exercise \(\PageIndex{1}\)

    What is the difference between uncertainty and vagueness?

    Exercise \(\PageIndex{2}\)

    Name some examples of fuzzy concepts.

    Exercise \(\PageIndex{3}\)

    Rough ontologies contain rough concepts. Describe how they are approximated in an OWL ontology.

    Exercise \(\PageIndex{4}\)

    Name two conceptually distinct ways how time can be dealt with in/with ontologies.

    Exercise \(\PageIndex{5}\)

    The introductory paragraph of Section 10.1: Uncertainty and Vagueness lists a series of examples. State for each whether it refers to uncertainty or vagueness.

    Exercise \(\PageIndex{6}\)

    Devise an example similar to the ‘minor’ and ‘young’, but then for ‘senior citizen’, ‘old’ and ‘old person’. Compare this with another student. How well do you think fuzzy ontologies will fare with respect to 1) ontologies in information systems and 2) the original aim of ontologies for information systems?

    Answer

    They do not fare well in case 2 (for ISs), at least not in theory. First, because of the reliance on concrete domains. Second, the numbers you and your classmate had chosen for ‘old’ was likely not the same—it certainly wasn’t for the students in one of my classes and the cut-off point I had in mind!—which then raises the general question as to what to do with something like such a number difference when faced with choosing to reuse an ontology and when aligning ontologies or integrating system. Conversely, it thus may work well for a particular application scenario (case 1, in ISs), assuming all its users agree on the fuzzy membership functions.

    Exercise \(\PageIndex{7}\)

    There are a few temporal reasoners for DLs and OWL. Find them (online) and assess what technology they use. As to the temporal things one can model, you may want to try to find out as follows: define a small example and see whether it can be represented and the deductions obtained in each of the tools.

    Answer

    Chronos and PROTON are relatively easy to find. Chronos uses constraint satisfaction for the temporal component, PROTON is based on Prolog. Chronos uses the 4d-fluents approach (i.e.: perdudantist [recall Chapter 6]) and implements a reasoner for the Allen relations. PROTON uses intervals and extends the situation calculus.

    Exercise \(\PageIndex{8}\)

    The Time Ontology was standardised recently. Inspect it. Can this be a viable alternative to \(\mathcal{DLR_{US}}\)?

    Answer

    No. (this is basically the same as the previous review question). Consider also the scope, as state in the W3C standard, with emphasis added: “OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars.”. That is: for annotations, but not for reasoning with it.

    Exercise \(\PageIndex{9}\)

    BFO draft v2.1 has all those indications of time in the names of the object properties. Compare this to the Time Ontology and something like \(\mathcal{DLR_{US}}\) or TDL-Lite. What would your advice to its developers be, if any?

    This page titled 11.3: Exercises is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Maria Keet via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.