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Rules in SW

Rules in SW. Semantic Web - Spring 2008 Computer Engineering Department Sharif University of Technology. Outline. Introduction to the rules and their usage in Web RuleML SWRL. Introduction.

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Rules in SW

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  1. Rules in SW Semantic Web - Spring 2008 Computer Engineering Department Sharif University of Technology

  2. Outline • Introduction to the rules and their usage in Web • RuleML • SWRL

  3. Introduction • Rules as an important aspect of Internet e-business: rule-based business policies & business processes, for B2B & B2C. • represent seller’s offerings of products & services, capabilities, bids; map offerings from multiple suppliers to common catalog. • represent buyer’s requests, interests, bids; → matchmaking. • represent sales help, customer help, procurement, authorization/trust, brokering, workflow. • high level of conceptual abstraction; easier for non-programmers to understand, specify, dynamically modify & merge. • executable but can treat as data, separate from code • potentially ubiquitous; already wide: e.g., SQL views, queries. • Rules in communicating applications, e.g., embedded intelligent agents.

  4. Introduction (cont.) • Rules are being used for many interconnected purposes, capturing regularities in application domains such as the following: • Engineering: Diagnosis rules • Commerce: Business rules (including XML versions such as the Business Rules Markup Language (BRML) of IBM's Business Rules for Electronic Commerce project) • Law: Legal reasoning (Robert Kowalski and Marek Sergot have been formalizing legal rules in an Imperial College group) • Internet: Access authentication (Tim Berners-Lee proposed registration engines that use authentication rules such as the following: Any person who was some time in the last 2 months an employee of an organization which was some time in the last 2 months a W3C member may register.)

  5. Expert Systems… • Are a branch of artificial intelligence. • Simulate human reasoning in some domain. • “Reason” by heuristic or approximate methods. • Explain and justify solutions in user-friendly terms.

  6. Types Of Expert Systems • Neural Networks • Blackboard Systems • Belief (Bayesian) Networks • Case-Based Reasoning • Rule-Based Systems

  7. Rule-Based Expert Systems • Originated from AI research in the 70s and 80s. • Problem data stored as facts. • “Reason” using IF…THEN…ELSE rules. • Can “reason” deductively (forward-chaining) or inductively (backward-chaining).

  8. When to Use Rule-Based Systems • Problem Domain = narrow, well-understood domain theory • Knowledge Representation = facts and rules • Output = recommendation • Explanation = rule firing trace • Learning Ability = generally no (but…)

  9. Inference Process • Rules and facts compared using pattern matcher. • Matched rules activated into a conflict set. • Conflict set resolved into agenda (process called conflict resolution). • Rule engine fires on agenda. • Engine cycles until all rules are satisfied.

  10. Rules in SW

  11. Example rules • The discount for a customer is 5.0 percent if the customer is premium and the product is regular • A customer is premium if their spending has been min 5000 euro in the previous year • Those who are members of CE can access CE portal

  12. RuleML • Rather than reinventing rule principles and markups in each community, the idea of RuleML is to 'package' the rule aspect of any domains • make it available as an (XML) namespace, .../RuleML, • can be mixed with a namespace for natural-language (XHTML) texts • and possible domain-specific namespaces (much like MathML is mixed into such domain texts).

  13. RuleML initiatives • Dozens of institutions (~35), researchers; esp. in US, EU • Mission: Enable semantic exchange of rules/facts between most commercially important rule systems • Standards specification: 1st version 2001; basic now fairly stable • A number of tools (~12 engines, translators, editors), demo applications • Successful Workshop on Rules at ISWC was mostly about RuleML / LP • Has now a “home” (www.ruleml.org) • Initial Core: Horn Logic Programs KR …Webized (in markup)… and with expressive extensions

  14. Type of rules possible in RuleML • Derivation rules • Deriving new facts • Maybe evaluated bottom-up as in deductive databases • Top-down as in Logic Programming • Example: dynamic inclusion of derived facts in a html response • Reaction rules • Also called ECA (Event-Condition-Action) or Triggers • Example: Specification of behavior in response to browser events

  15. RuleML top-level hierarchy • Integrity constraints are considered as "denials" or special reaction rules whose only possible kind of action is to signal inconsistency when certain conditions are fulfilled. • Derivation rules are considered as special reaction rules whose action happens to only add or 'assert' a conclusion when certain conditions (premises) are fulfilled. • Facts are considered as special derivation rules that happen to have an empty (hence, 'true') conjunction of premises.

  16. RuleML toturial • Peter Miller's spending has been min 5000 euro in the previous year. • <Atom> <Rel>spending</Rel> <Ind>Peter Miller</Ind> <Ind>min 5000 euro</Ind> <Ind>previous year</Ind> </Atom> • This is a fact

  17. RuleML toturial (cont.) • "spending" is marked up as the relation name (table name) for the fact. • "Peter Miller", "min 5000 euro", and "previous year" are marked up as individual constants that are the three arguments (table columns) of the relation. • The entire relation application constitutes an atomic formula, marked up by <Atom> ... </Atom>.

  18. Representing as tree

  19. An example rule • A customer is premium if their spending has been min 5000 euro in the previous year <Implies> <head> <Atom> <Rel>premium</Rel> <Var>customer</Var> </Atom> </head> <body> <Atom> <Rel>spending</Rel> <Var>customer</Var> <Ind>min 5000 euro</Ind> <Ind>previous year</Ind> </Atom> </body> </Implies>

  20. In tree form

  21. What is SWRL? • SWRL is an acronym for Semantic Web Rule Language. • SWRL is intended to be the rule language of the Semantic Web. • SWRL includes a high-level abstract syntax for Horn-like rules. • All rules are expressed in terms of OWL concepts (classes, properties, individuals).

  22. SWRL Characteristics • W3C Submission in 2004: http://www.w3.org/Submission/SWRL/ • Based on OWL-DL • Has a formal semantics • Rules saved as part of ontology • Increasing tool support: Bossam, R2ML, Hoolet, Pellet, KAON2, RacerPro, SWRLTab • Can work with reasoners

  23. OWL extension for rules (SWRL) • A recommendation to extend OWL to create a Rule language

  24. SWRLTab • A Protégé-OWL development environment for working with SWRL rules • Supports editing and execution of rules • Extension mechanisms to work with third-party rule engines • Mechanisms for users to define built-in method libraries • Supports querying of ontologies

  25. References • RuleML.org • http://www.semanticweb.org/SWWS/program/full/paper20.pdf • http://www.cs.man.ac.uk/~horrocks/DAML/Rules/ • http://www.cs.unb.ca/~boley/ruleml/ruleml-rgs.pdf • http://www.w3.org/Submission/SWRL/

  26. The End

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