The first shortcut notation lets one write long rif:iri constants in the form prefix:title, where prefix is a short name that old boyfriendpands into an IRI according to a suitable Prefix directive. For instance, ex:child would expand into the rif:iri constant ""^^rif:iri, if ex is defined as in the Prefix(ex . ) directive below. The second shortcut notation uses angle brackets as a way to shorten the ". "^^rif:iri idiom. For instance, the prevous rif:iri constant can be alternatively represented as < The last shortcut notation lets one write rif:iri constants using IRIs relative to a base, where the base IRI is specified in a directive. For instance, with the directive, below, both
The aforementioned RIF algorithms try (admittedly uncomfortable) analytical renderings of your pursuing the statements out of Shakespeare’s Hamlet: “Anything was rotten about condition regarding Denmark,” “To-be, or not are,” and you will “All of the guy possess providers and you may appeal.”
Observe that the above set of formulas has a nested subset with its own annotation,
The first document, below, bicupid coupon imports the second document, which is assumed to be located at the IRI In addition, the first document has references to two remote modules, which are located at and correspondingly. Such segments is actually assumed as education bases that provide the latest common information regarding college or university enrollment, courses offered in some other semesters, and stuff like that. The principles comparable to the fresh new remote segments are not revealed, as they do not instruct additional features. About best situation, these education bases could only getting groups of situations into predicates/structures who supply brand new required guidance.
In this example, the main document contains three rules, which define the predicates u:requires, u:instructs and u:popular_way. The information for the first two predicates is obtained by querying the remote modules corresponding to Universities 1 and 2. The rule that defines the first predicate says that if the remote university knowledge base says that a student s takes a course c in a certain semester s then takes(s c s) is true in the main document. The second rule makes a similar statement about professors teaching courses in various semesters. Inside the main document, the external modules are referred to via the terms _univ(1) and _univ(2). The Component directives tie these references to the actual locations. The underscore in front of univ signifies that this is a rif:regional symbol and is a shortcut for "univ"^^rif:local, as defined in [RIF-DTB], Section Constants and Symbol Spaces. Note that the remote modules use frames to represent the enrollment information and predicates to represent course offerings. The rules in the main document convert both of these representations to predicates. The third rule illustrates a use of aggregation. The comprehension variable here is ?Stud and ?Crs is a grouping variable. Note that these are the only free variables in the formula over which aggregation is computed. For each course, the aggregate counts the number of students in that course over all semesters, and if the number exceeds 500 then the course is declared popular. Note also that the comprehension variable ?Stud is bound by the aggregate, so it is not quantified in the Forall-prefix of the rule.
I train formulas, and documents and groups, with the pursuing the done example (with apologies so you can Shakespeare on imperfect rendering of the meant meaning within the reason)
The imported document has only one rule, which defines a new concept, u:studentOf (a student is a studentOf of a certain professor if that student takes a course from that professor). Since the main document imports the second document, it can answer queries about u:studentOf as if this concept were defined directly within the main document. ?