Dr. Bhavani Thuraisingham

# Dr. Bhavani Thuraisingham

## Dr. Bhavani Thuraisingham

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##### Presentation Transcript

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

2. 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

3. 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

4. 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

5. 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)

6. 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

7. 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

8. 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.

9. 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 <fact> <atom> <predicate>p</predicate> <term> <const>a</const> <term> <atom> </fact>

10. Policies in RuleML <fact> <atom> <predicate>p</predicate> <term> <const>a</const> <term> <atom> Level = L </fact>

12. 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

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

14. 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

15. Inference/Privacy Control Interface to the Semantic Web Technology By UTD Inference Engine/ Rules Processor Policies Ontologies Rules Rules Data Rule-based Data Management

16. 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)

17. 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

18. 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

19. 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

20. 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 Elsevier’s direction

21. 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

22. 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

23. 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., John’s 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 • - - - -

24. Skill Finding at Swiss Life • Ontologies are being developed to specify the skills and query languages to query the ontologies • E.g. <owl: Class rdf: ID = “Publishing”> <rdfs: subClassOf rdf: resource = ‘#Skills”/> </owl: Class> <owl: Class rdf: ID = “Skills”> • - - - • </owl: Class>

25. 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?”

26. 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 • - - - -

27. 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.

28. 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

29. 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.) • DAML+OIL • W3C community started with DAML+OIL for ontology specifications and developed OWL • E.g., <profile: ServiceProvider rdf ID = “Sportsnews”> • - - - - • </profile: ServiceProvider>

30. 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

31. 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

32. Device Interoperability at Nokia • Nokia’s 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

33. 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