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Building Trustworthy Semantic Webs Lecture #9: Logic and Inference Rules Dr. Bhavani Thuraisingham September 18, 2006 Objective of the Unit This unit will provide an overview of logic and inference rules component of the semantic web and then discuss some security issues

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Building Trustworthy

Semantic Webs

Lecture #9: Logic and Inference Rules

Dr. Bhavani Thuraisingham

September 18, 2006

objective of the unit
Objective of the Unit
  • This unit will provide an overview of logic and inference rules component of the semantic web and then discuss some security issues
outline of the unit
Outline of the Unit
  • What are logic and inference rules
  • Why do we need rules?
  • Example rules
  • Logic programs
  • Monotonic and Nonmonotoic rules
  • Rule Markup
  • Example Rule Markup in XML
  • Policy Specification
  • Relationship to the Inference and Privacy problems
  • Summary and Directions
logic and inference
Logic and Inference
  • First order predicate logic
  • High level language to express knowledge
  • Well understood semantics
  • Logical consequence - inference
  • Proof systems exist
  • Sound and complete
  • OWL is based on a subset of logic – descriptive logic
why rules
Why Rules?
  • RDF is built on XML and OWL is built on RDF
  • We can express subclass relationships in RDF; additional relationships can be expressed in OWL
  • However reasoning power is still limited in OWL
  • Therefore the need for rules and subsequently a markup language for rules so that machines can understand
example rules
Example Rules
  • Studies(X,Y), Lives(X,Z), Loc(Y,U), Loc(Z,U)  HomeStudent(X)
  • i.e. if John Studies at UTDallas and John is lives on Campbell Road and the location of Campbell Road and UTDallas are Richardson then John is a Home student
  • Note that

Person (X)  Man(X) or Woman(X) is not a rule in predicate logic

That is if X is a person then X is either a man of a woman. This can be expressed in OWL

However we can have a rule of the form

Person(X) and Not Man(X)  Woman(X)

monotonic rules
Monotonic Rules
  •  Mother(X,Y)
  • Mother(X,Y)  Parent(X,Y)

If Mary is the mother of John, then Mary is the parent of John

Syntax: Facts and Rules

Rule is of the form:

B1, B2, ---- Bn  A

That is, if B1, B2, ---Bn hold then A holds

logic programming
Logic Programming
  • Deductive logic programming is in general based on deduction
    • i.e., Deduce data from existing data and rules
    • e.g., Father of a father is a grandfather, John is the father of Peter and Peter is the father of James and therefore John is the grandfather of James
  • Inductive logic programming deduces rules from the data
    • e.g., John is the father of Peter, Peter is the father of James, John is the grandfather of James, James is the father of Robert, Peter is the grandfather of Robert
    • From the above data, deduce that the father of a father is a grandfather
  • Popular in Europe and Japan
nonmonotonic rules
Nonmonotonic Rules
  • If we have X and NOT X, we do not treat them as inconsistent as in the case of monotonic reasoning.
  • For example, consider the example of an apartment that is acceptable to John. That is, in general John is prepared to rent an apartment unless the apartment ahs less than two bedrooms, is does not allow pets etc. This can be expressed as follows:
  •  Acceptable(X)
  • Bedroom(X,Y), Y<2  NOT Acceptable(X)
  • NOT Pets(X)  NOT Acceptable(X)
  • Note that there could be a contradiction. But with nonmotonic reasoning this is allowed.
rule markup
Rule Markup
  • The various components of logic are expressed in the Rule Markup Language – RuleML
  • Both monotonic and nonmonotnic rules can be represented
  • Example representation of Fact P(a) - a is a parent









policies in ruleml
Policies in RuleML








Level = L


example policies
Example Policies
  • Temporal Access Control
    • After 1/1/05, only doctors have access to medical records
  • Role-based Access Control
    • Manager has access to salary information
    • Project leader has access to project budgets, but he does not have access to salary information
    • What happens is the manager is also the project leader?
  • Positive and Negative Authorizations
    • John has write access to EMP
    • John does not have read access to DEPT
    • John does not have write access to Salary attribute in EMP
    • How are conflicts resolved?
privacy policies
Privacy Policies
  • Privacy constraints processing
    • Simple Constraint: an attribute of a document is private
    • Content-based constraint: If document contains information about X, then it is private
    • Association-based Constraint: Two or more documents taken together is private; individually each document is public
    • Release constraint: After X is released Y becomes private
  • Augment a database system with a privacy controller for constraint processing
system architecture for access control
System Architecture for Access Control














RuleML Data


ruleml data management
RuleML Data Management
  • Data is presented as RuleML documents
  • Query language – Logic programming based?
  • Policies in RuleML
  • Reasoning engine
    • Use the one developed for RuleML
inference privacy control
Inference/Privacy Control

Interface to the Semantic Web



Inference Engine/

Rules Processor




Rules Data

Rule-based Data Management

summary and directions
Summary and Directions
  • Rules have expressive and reasoning power
  • Handles some of the inadequacies of OWL
  • Both monotonic and nonromantic reasoning
  • Logic programming based
  • Policies specified in RulesML
  • Need to build an integrated system