Collection of general data mining briefings

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Building Trustworthy Semantic Webs Lecture 11
Logic and Inference Rules Semantic Web
Applications
Dr. Bhavani Thuraisingham
October 1, 2008

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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
• Part II Semantic Web Applications

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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

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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

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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)

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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

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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

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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), Ylt2 ? NOT Acceptable(X)
• NOT Pets(X) ? NOT Acceptable(X)
• Note that there could be a contradiction. But
  with nonmotonic reasoning this is allowed.

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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
• ltfactgt
• ltatomgt
• ltpredicategtplt/predicategt
• lttermgt
• ltconstgtalt/constgt
• lttermgt
• ltatomgt
• lt/factgt

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Policies in RuleML
ltfactgt ltatomgt ltpredicategtplt/predicategt
lttermgt ltconstgtalt/constgt
lttermgt ltatomgt Level L lt/factgt
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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?

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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

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System Architecture for Access Control
User
Pull/Query
Push/result
RuleML- Access
RuleMF- Admin
Admin Tools
Credential base
Policy base
RuleML Data Documents
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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

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Inference/Privacy Control
Interface to the Semantic Web
Technology By UTD
Inference Engine/ Rules Processor
Policies Ontologies Rules
Rule-based Data Management
Rules Data
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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
• Other rules SWRL (semantic web rules language)

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Semantic Web Applications

• Discussion of applications
• Horizontal Information Products at Elsevier
• Data integration at Audi
• Skill finding at Swiss Life
• Think Tank Portal at EnterSearch
• E-Learning
• Web Services
• Multimedia Collection at Scotland Yard
• Online Procurement at Daimler Chrysler
• Device Interoperability at Nokia
• Common threads and challenges

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Types of Application

• Horizontal Information Products at Elsevier
  Integration
• Data integration at Audi Integration
• Skill finding at Swiss Life Search
• Think Tank Portal at EnterSearch Knowledge man
  agent
• E-Learning Knowledge management
• Web Services Web services (for any of the other
  applications discussed)
• Multimedia Collection at Scotland Yard Searching
• Online Procurement at Daimler Chrysler
  E-Business
• Device Interoperability at Nokia
  Interoperability

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Horizontal Information Products at Elsevier

• Elsevier is publishing company based in Amsterdam
• E.g., publisher of Computer Standards and
  Interface Journal that has papers on all kinds of
  computer related standards
• Currently the journals and books are grouped by
  topics such as say operating systems, databases,
  etc. (or at a higher level, Biology, Chemistry,
  etc.)
• Where do we then put the journal Computer
  Standards and Interfaces?
• Need horizontal groupings also

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Horizontal Information Products at Elsevier

• Semantic web technologies are being used by
  Elsevier
• RDF for document representation
• RDF for ontologies
• Query language based on RDF to query the
  documents and the ontologies
• E.g. Life Science Thesaurus EMTREE
• Other publishing companies are following in
  Elseviers direction

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Data Integration at Audi

• Integrate the data in multiple data sources to
  provide better customer relationship management
  and other services to improve profits
• The databases are disparate and heterogeneous
• Many current operations are carried out manually
• Expensive and missed opportunities

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Data Integration at Audi

• Ontolotues are being specified to address
  semantic heterogeneous
• E.g., SLR is a type of camera one applications
  calls it SLR, another application calls it
  Olympus-OM-10
• When the latter application encounters the term
  SLR, it will query the ontology and determine
  that SLR is a camera
• Details are given in Chapter 6

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Skill Finding at Swiss Life

• Swiss Life is an insurance company that developed
  a system to find all the skills in the company
• E.g., Johns skills are on data management,
  ontology management
• Challenging problem as people have multiple
  skills for different applications
• Need the following capabilities
• Cross listing of skills
• Querying skills
• - - - -

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Skill Finding at Swiss Life

• Ontologies are being developed to specify the
  skills and query languages to query the
  ontologies
• E.g.
• ltowl Class rdf ID Publishinggt
• ltrdfs subClassOf rdf resource Skills/gt
• lt/owl Classgt
• ltowl Class rdf ID Skillsgt
• - - -
• lt/owl Classgt

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Think Tank Portal at EnterSearch

• EnterSearch is a consortium of corporations in
  Europe that provide IT for the energy companies
• Similar to MCC in Austin TX
• EnterSerach Portal currently describes the
  various research projects, papers etc.
• XML representation is used for describing the web
  content
• Need to represent semantics so that the
  corporations can get answers to useful questions
  of the form
• where do I put my computing resources to solve a
  problem?

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Think Tank Portal at EnterSearch

• Semantic web technologies are being utilized in
  particular ontoogies are developed for the
  following
• Hardware
• Software
• Communications
• E-Commerce
• Agents
• Market/Auction
• Resource Allocation
• - - - -

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E-Learning

• With the Internet and the web, we now have
  on-line universities, course offerings, tutoring
  etc.
• Students should have the choice for selecting
  various courses in the order they want, provide
  they take the prerequisites
• Semantic web technologies enable flexible access
  as well as integration of various data sources
  and processes to enable learning
• Ontologies are being developed for learning
  applications
• E.g., Contents of the courses
• Description of the courses etc.

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Web Services

• Web services can be utilized by any of the other
  applications discussed in this unit (e.g.,
  Elsevier, Audi etc.)
• We services are invoked to carry out functions on
  the web including find locations, search for
  documents etc.
• Simple services and compound services
• Three components to the service
• Service profile Description of the service
  what it does
• Serviced model how it does it
• Service groundings protocol for invoking the
  service

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Web Services

• DAML and DAML-S developed by the DARPA community
  combined with the European community developing
  OIL focused on ,languages for web services
• Semantic of the web services (e.g., reasoning
  about the services, why certain actions are taken
  etc.)
• DAMLOIL
• W3C community started with DAMLOIL for ontology
  specifications and developed OWL
• E.g.,
• ltprofile ServiceProvider rdf ID Sportsnewsgt
• - - - -
• lt/profile ServiceProvidergt

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Multimedia Collection Indexing at Scotland Yard

• Scotland Yard uses a database to keep track of
  the antiques that are stolen
• While sophisticated indexing techniques have been
  developed, there is a problem with semantics
• E.g., Red cushioned chair could also be described
  as Queen Anne chair
• Ontologies for describing semantics
• Need more details of the project

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On-line Procurement at Daimler Chrysler

• Daimler Chrysler interacts with numerous
  suppliers to develop a product
• Standards developed by Rosetta.Net for E-Business
  are being used for interoperability
• XML syntax, no semantics of the product
  descriptions are available
• Ontologies for describing the various product
  descriptions including the semantics are the long
  term goal for seamless integration of the supply
  chain operation
• Need more details of the project

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Device Interoperability at Nokia

• Nokias objective is to integrate multiple
  devices (cell phone, PDA, cars, laptop etc) to
  provide a pervasive computing environment
• Objects is to locate the various services and
  understand the different devices and their
  functions
• Need to describe the various services
• Current technology provides syntactic
  descriptions
• Semantic web technologies, through ontologies
  enable the understanding the devices and reasons
  about their functions
• Need more details of the project

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Common Threads and Challenges

• Common Threads
• Building Ontologies for Semantics
• XML for Syntax
• Challenges
• Scalability, Resolvability
• Security policy specification, Securing the
  documents and ontologies
• Developing applications for secure semantic web
  technologies
• Automated tools for ontology management
• Creating, maintaining, evolving and querying
  ontologies