Logic Foundation and Services for Semantic Web

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Logic Foundation and Services for Semantic Web
Zhongzhi Shi shizz_at_ics.ict.ac.cn Institute of
Computing Technology Chinese Academy of Sciences
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Outline

• Introduction
• Description Logic
• Dynamic Description Logic
• Agent-based Services
• Ontology-based Knowledge Management KMSphere
• Conclusions

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Semantic Web

• Web was invented by Tim Berners-Lee (amongst
  others), a physicist working at CERN
• His vision of the Web was much more ambitious
  than the reality of the existing (syntactic) Web

a plan for achieving a set of connected
applications for data on the Web in such a way as
to form a consistent logical web of data
an extension of the current web in which
information is given well-defined meaning, better
enabling computers and people to work in
cooperation
This vision of the Web has become known as the
Semantic Web
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Semantics Is Important

• Avoid transformation code between data sets
• Unambiguously capture service profiles
• Enable dynamic discovery of services
• Use reasoners to locate services in yellow
  pages
• Enable dynamic collaboration of services
• Use reasoners to infer service descriptions and
  capabilities
• Enable rich, automatic, service orchestration
• Process layer will be far more automated with
  semantics

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Adding Semantic Markup

• Extend existing rendering markup with semantic
  markup
• Metadata annotations that describe
  content/function of web accessible resources
• Using Ontologies to provide vocabulary for
  annotations
• Formal specification is accessible to machines
• Semantics given by ontologies

Make web resources more accessible to automated
processes
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Ontology

• In philosophy, an ontology is a theory about the
  nature of existence.
• An ontology is a document or file that formally
  defines the relations among terms.
• An ontology is a formal, explicit specification
  of a shared conceptualization.
• The most typical kind of ontology for the Web has
  a taxonomy and a set of inference rules.

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The Semantic Web layer cake by Tim Berners-Lee
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Web Ontology Language OWL

• W3C Recommendation 10 Feb 2004. Deborah L.
  McGuinness and Frank van Harmelen eds. formerly
  Feature Synopsis for OWL Lite and OWL
• OWL facilitates greater machine interpretability
  of Web content than that supported by XML, RDF,
  and RDF Schema (RDF-S) by providing additional
  vocabulary along with a formal semantics.
• OWL has three increasingly-expressive
  sublanguages OWL Lite, OWL DL, and OWL Full.

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The Ontology Language Stack
OWL
DAML-S
DAML-R
DAMLOIL
OIL
DAML-Ont
DC
PICS
RDF Schema
RDF
XOL
HTML
XML Name Space XML Schema
Unicode
URI
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OWL

• Ability to be distributed across many systems
• Scalability to Web needs
• Compatibility with Web standards for
  accessibility and internationalization
• Openess and extensiblility

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OWL Reasoners

• KAON2 is a reasoner for OWL extended with the
  DL-safe subset of SWRL it also provides an OWL
  API.
• FaCT -- a DL reasoner. see WonderWeb project,
  Bechhofer 15 Sep.
• Racer -- a DL reasoner. see Horrocks 12Sep
• Cerebra from Network Inference - owl syntax
  checker, nearly complete OWL DL Horrocks 12Sep
• cwm -- useful but incomplete OWL Full
• Euler -- useful but incomplete OWL Full,see De
  Roo 11 Jul 51 / 234 tests
• surnia -- OWL full reasoner based on otter. Hawke
  26Aug
• Jena/HP ( Reynolds/HP 7 May)will support OWL
  reasoning.
• Vampire Horrocks 17 Jul - uses a first-order
  theorem prover to do OWL DL
• Pellet is a reasoner built in Java that was
  designed specifically for OWL reasoning.
  Hendler/Sirin/Parsia 15Sep).
• SWI-Prolog Semantic Web Library contains owl.pl -
  an OWL reasoning package.
• F-OWL is an f-logic based Owl tool from UMBC.
• E-wallet is an e-commerce and mobile computing
  tool based on a rule-based OWL reasoner.

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Outline

• Introduction
• Description Logic
• Dynamic Description Logic
• Agent-based Services
• Ontology-based Knowledge Management KMSphere
• Conclusions

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Description Logics

• Consistency query results, across vendor
  implementations and instances, should be
  consistent
• Performance Although performance metrics depend
  on model constructs, OWL-DL supports highly
  optimized inference algorithms
• Predictable semantics are mathematically
  decidable within the model, reasoning is finite
• Foundational provides a baseline inside
  applications for layered semantic models
• Reliability if the answer to a query is implied
  by any of the model data, it will be found
  guaranteed.

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Description Logic

• Frame-based system
• Semantic Network
• Object-oriented representation
• Semantic data models
• Ontology language

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Description Logic

• Concepts and Role
• TboxAssertions
• AboxInstance
• Reasoning mechanism in terms of Tbox and Abox

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TBox
TBox Language Set of axioms
Definition Concept name A C, A ? C Father
Man ? ? has-child.Human Human ? Animal ? Biped
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TBox Instance
? Concept entity (one unit predicate,class) Exam
pleStudent, Married x Student(x)
,x Married(x) Bird ? Animal, Man ? Human
? Roles Property (two unit predicate,role) Examp
lesFriend,Loves ltx,ygt Friend(x,y) ,ltx,ygt
Loves(x,y)
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ABox Language(Assertion)
Set of concrete axioms
? Concept assertion aC ExamplesTom is a
student Tom Student Or Student(Tom) John
Man ? ? has-child.Female
? Role assertion Indicate the role between two
objects lta,bgtR ExampleJohn has a child called
Mary ltJohn, Marygt has-child
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Syntax and Semantics
Operator syntax syntax semantics example
Atomic concept A A AI ? ?I Human
Atomic role R R RI ??I ? ?I has-child
On concept C,D and role R On concept C,D and role R On concept C,D and role R On concept C,D and role R On concept C,D and role R
conjunction conjunction C? D CIn DI Human ? Male
disjunction disjunction C? D CI ? DI Doctor ? Lawyer
Negative Negative C ?I \C Male
Exist Exist ? R.C x ? y.ltx,ygt? RI?y ? CI ? has-child.Male
all all ? R.C x ? y.ltx,ygt? RI ? y ? CI ? has-child.Doctor
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Reasoning in DL

• 1) Subsumption
• 2) consistency
• 3) satisfiability
• 4) instance checking

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K B
TBox(Scheme) Man Human ? Male Happy-father
Human ? ? Has-child.Female?
Abox(Data) John Happy-father ltJohn,Marygt
Has-child
Reasoning
Interface
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Outline

• Introduction
• Description Logic
• Dynamic Description Logic
• Agent-based Services
• Ontology-based Knowledge Management KMSphere
• Conclusions

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Dynamic Description Logic

• The primitive symbols
• Concept nameC1, C2,
• Role nameR1, R2,
• Individual constanta, b, c,
• Individual variablex, y, z,
• Concept operation?, ?, ?, ?, ?
• Axiom operation?, ?, ??
• ActionA1, A2,
• Action constraction(composition),?
  (alternation), (repeat),?(test)
• Action variablea,ß,
• Axiom variable?, ?, p,
• State variableu, v, w,

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Dynamic Description Logic

• Concepts in DDL are defined as the following
• (1) Primitive concept P, top ? and bottom ? are
  concepts.
• (2) ?C, C?D, C?D are concepts.
• (3) ?R.C, ?R.C are concepts.

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Dynamic Description Logic
An action description is the form of
where (1) A is the action name. (2) x1, , xn
are individual variables, which denote the
objects the action operate on. (3) PA is the set
of preconditions, which must be satisfied before
the action is executed. (4) EA is the set of
results, which denote the effects of the action.
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DDL Semantics

• Actions in DDL are defined as the following
• Atom action A(a1, , an) is action.
• If a and ß are actions, then aß, a?ß, a are
  actions
• If ? is an assertion formula, then ? ? is
  action.

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Outline

• Introduction
• Description Logic
• Dynamic Description Logic
• Agent-based Services
• Ontology-based Knowledge Management KMSphere
• Conclusions

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Semantic Web Services

• Define exhaustive description frameworks for
  describing Web Services and related aspects (Web
  Service Description Ontologies)
• Support ontologies as underlying data model to
  allow machine supported data interpretation
  (Semantic Web aspect)
• Define semantically driven technologies for
  automation of the Web Service usage process (Web
  Service aspect)

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OWL-S
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OWL-S Context
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Service Description Language SDLSIN

• ltasdl-descrgt(ctype
• service-name name
• context context-name
• types (type-name ltmodifiergt
  type)
• isa name
• inputs (variable ltmodifiergt
  put-type-name)
• outputs (variable ltmodifiergt
  put-type-name)
• input-constraints
  (constraint)
• output-constrains
  (constraint)
• io-constrains (constraint)
• concept-description
  (ontology-name ontology-body)
• state-language name
• concept-language name
• attributes (attributes-name
  attributes-value)
• text-description name
• )

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OWL-S
OWL-S Interpreter
DDL
Incidences matrixDDL
OWL-S
Petri Net Generator
Petri Net Analysor
Services
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Agent-based Services
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Agent Architecture
Agent kernel
Function Module Interface
Function Component
Sensor
Engine
Plug-INs
Plug-in Manager
Reasoning
Communicator
Negotiation
Scheduling
coOperation
Resource Database
Task Database
others
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Metal State Model

• Mental State ltK, A, G, P, I gt,
• Where
• K belief
• A action
• G goal
• P plan
• I intention?

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Multiagent Environment MAGE
Requirement Analysis
System Development
System Deployment
System Design
Behaviour Library
Agent Society
Agent Library
AUMP
VAStudio
MAGE Running Support
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Outline

• Introduction
• Description Logic
• Dynamic Description Logic
• Agent-based Services
• Ontology-based Knowledge Management KMSphere
• Conclusions

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Ontology Development
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KMSphere
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KMSphere
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KMSphere
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KMSphere
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KMSphere Demo
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KMSphere Demo
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KMSphere Demo
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KMSphere Demo
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KMSphere Demo
???????
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KMSphere Demo
RDQL (RDF Data Query Language)??
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Agent Grid Intelligence Platform AGrIP
E-B
E-G
IE
DSS
IB
Simul
Corl
Diag.
Information Sourses
Applications
Web
Middelware
GIS
CBR
Databases
GHunt
OKPS
CAD
MSMiner
MIRES
KMSphere
Stream Media
MAGE
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Emergency Interactive Systsem GEIS
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Distributed Data Mining
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Conclusions

• The dynamic description logic is a good logic
  foundation for Semantic Web.
• Semantic Web services in terms of agents
• Ontology-based knowledge management system
  KMSphere provides knowledge sharing to users.

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