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This part lists the requirements on RIF implied by the submitted use cases (cf. Use Cases) and the analysis of the use case categories (cf. General Use Case Categories). Duplicates were eliminated as much as possible and classes of requirements were introduced so as to improve readability.

Note that the order of stated (classes of) requirements doesn't mirror their relevance to the RIF or to the phases stated in the charter.

The list of requirements reflects the status of the use cases on Friday, February 10, 2006.

Requirements on RIF


Compatibility with RDF & OWL


Different kinds of rules (deductive, normative, reactive)


Data model / Type of data

Basic numeric computations & aggregations

Procedural attachements

Transfer of rules


Closed world assumption scoped to data sets (this is also implied by the requirement on scoped negation)

Representation of probabilistic, uncertain information and degrees of truth

Meta-reasoning / Evolution of rule sets

Complex event processing

Datatype support

Query language

Modules of rules

Validation & verification

Priorities and preferences

Type system / Mode declarations

Distribution & Scalability

The rest of the initial requirements list (not yet classified requirements)

An instantiation of this use case was implemented with POSL rules as NBBizKB and tested in OOjDREW. The need to construct such integration rules through iterative refinement with human experts implies the requirement of a human-readable syntax.

In this use case, the identity criterion for businesses across the Web sources is a problem if no URI is provided or URI normalization cannot be done: normalized phone numbers needed to be used in NBBizKB. This implies the requirement to 'webize' the language with URIs and interface it to the newest official URI normalization algorithm.

Given that the same business can be identified in both sources, and assuming it is correctly classified w.r.t. their respective taxonomies, an alignment between the two taxonomic classes can be hypothetically established, which becomes the stronger the more such business-occurrence pairs can be found in both sources. This implies the requirement to combine rules with taxonomies and to permit uncertainty handling, as explored in Fuzzy RuleML.

(from Information Integration with Rules and Taxonomies)

(from Interpretation and Interchange of Regulations)