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The Web Rule Language in its Context

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The Web Rule Language in its Context

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  1. A Web Rules WG Charter Focus Strawman ProposalVersion 1.1, April 30, 2005This Version Prepared by:Benjamin Grosof, Harold Boley, Michael Kifer, and Said Tabetof The RuleML Initiative (http://www.ruleml.org).Incorporating Comments by Ed Barkmeyer.***Further revisions to be incorporated from community discussion.***Modified from earlier version in RuleML Position Paper [96] of the W3C Workshop on Rule Languages for Interoperability, 27-28 April 2005.Responsive to the discussion from that Workshop, and from the WSMO-RuleML-SWSI Face-to-Face Meeting of 26 April 2005.“WG” above = W3C Working Group

  2. The Web Rule Language in its Context FOL++ Rules OWL RDF(S) XML Unicode URI

  3. Conclusions sanctioned do not depend on how executed, e.g., forward chaining has same semantics as backward chaining “Reaction” rules, that perform side-effectful actions, have a semantics which cleanly extends the basic case of rules that do not. Semantic Interoperability Principles - high level

  4. Kernel based on logical KR Semantics, syntax, layering: for that kernel Rudimentary rule management: e.g., queries, answers, premises, conclusions, updates to premises, ruleset definition, importation of rules, simple versioning, simple provenance Use Cases from Business Processes, Services Policies, in particular Support Semantic Web Services requirements, in particular Integrate Rules and Ontologies Interoperate with OWL, in particular Represent Ontologies as Rules, in particular Focus Overall of WG

  5. Semantic Web general, using XML and/or RDF encoding RDF- and OWL-centric, in particular Logic Program based, in particular Business Rules general, based on existing rule-based Production Rules, in particular Rule Communities Served

  6. Kernel KR Focus Declarative Logic Programs expressiveness including 1. Datalog Horn LP (N-ary predicates supported) 2. + scoped default negation applied to atoms a. simple extensional b. more general (allowing inferential chaining to establish the atom in question -- subset of, or full, Well Founded semantics) 3. + procedural attachments (external calls) a. actions (side-effectful – external) b. tests (side-effect-free queries) 4. + logical functions, incl. for object creation, skolemization a. limited initially(to ensure finite/tractable forward inferencing) b. more general(e.g., for backward chaining, “sugar” features)

  7. Kinds of Rules & Rule Systems Translatable/Reducible to Kernel Most other wish-list features can be expressively reduced to this core KR abstraction, for which Situated Ordinary Logic Programs can provide the semantics theory • OWL: large subset,  • OWL ontology integration via overlap of LP with Description Logic (e.g., use Description Logic Programs V2, with integrity constraints, skolemization, equality, passing of derived facts) • SWRL: large subset • Production Rules cf. PRRuleML: large subset (Production Logic Programs) • Decision trees • Decision tables • “Sequential” rules cf. PRR: [**probable, need to understand better] • Prolog: the pure subset (which is large) • SQL relational databases: large subset (incl. all core) • Event-Condition-Action rules: large subset

  8. Those are translatable/reducible because the following are …

  9. Additional “Sugar” Features that are Translatable/Reducible to Kernel Most other wish-list features can be expressively reduced* to this core KR abstraction, for which Situated Ordinary Logic Programs can provide the semantics theory (* with tractability, known techniques). E.g., much or all of the expressiveness in the following. • RDF facts • Frame syntax • Slotted syntax • Lists • (N-ary predicates if restrict core to 2-ary) • RDFS-DL simple ontologies • Datatyping: basic

  10. Sugar Features II • “Else” part of if-then-else • Courteous prioritized defaults, incl. declarative priorities, limited strong/classical negation, prioritized conflict handling, paraconsistency robustness • Default inheritance cf. Object Oriented programming, “frame” languages • “Hilog” – quasi higher order syntactic sugar • Lloyd-Topor • Integrity constraints that report violations • Anonymous existentials, blank-nodes, limited skolemization • “Crud” – create update delete, cf. Production Rules (restricted) • “Assert”, and basic “retract”, cf. Production Rules (restricted)

  11. Sugar Features III • Reification, basic • User equality, basic aspects • Equations, basic • Built-ins (side-effect-free functions/operators, read/write) • Access to surrounding object-oriented data environment, cf. OO Production Rules • Ontological context translation & mediation • Contextual selection conditions for whole rulesets • “Rules flow”: some (e.g., sequencing of rule groups) • … probably some more things we forgot to list here …

  12. The Web Rule Language in its Context FOL++ Rules OWL RDF(S) XML Unicode URI

  13. Layering Relationships wrt existing Semantic Web Standards subsumes (expressively) layers-on (makes use of) overlaps-with (expressively) Rules Kernel overlaps SWRL OWL-DL DLP RDFS-DL RDF XML

  14. Sugar Features vs. Kernel • “Sugar-enhanced” Languages can be translated into the kernel. • I.e., Sugar Features can be implemented/supported via translators • Including as “best practice”, etc. • Could consider doing some of them as part of WG proper • E.g., basic set of datatypes • … But it’s not as crucial

  15. Deliverables Desired • Abstract syntax • Semantics • Layering definitions: e.g., Datalog Horn layer • Concrete syntax: • Markup syntax in XML • RDF (e.g., RDF/XML) • Human-readable presentation (non-XML) syntax • UML/MOF metamodel • Some light ontology about rudimentary rule management, incorporated into the above • E.g., to enable representing provenance, or expressive restrictions met, about a particular rulebase

  16. Supported Tasks & Kinds of Knowledge • Policies: authorization, contracting, security, privacy, monitoring, advertising, regulations, governance, … • Validation: integrity, notification, … • Business Processes, Workflows, Protocols, … • Process modeling: Abstract State Machines, Pi-Calculus, … • Semantic Web Services • Ontologies • Mediation: map between ontologies/contexts • …

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