slide1
Download
Skip this Video
Download Presentation
An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University [email protected]

Loading in 2 Seconds...

play fullscreen
1 / 91

An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University [email protected] - PowerPoint PPT Presentation


  • 351 Views
  • Uploaded on

An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University [email protected]@cs.iastate.edu Outline Introduction & Motivation XML RDF & RDFS DL OWL Future Look & Resources World Wide Web WWW:

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University [email protected]' - paul


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
An Introduction to the

Semantic Web

Part 1: XML, RDF and RDFS

Jyotishman Pathak , AI Lab, Iowa State University

[email protected]@cs.iastate.edu

ISU Artificial Intelligence Research Laboratory

outline
Outline
  • Introduction & Motivation
  • XML
  • RDF & RDFS
  • DL
  • OWL
  • Future Look & Resources

ISU Artificial Intelligence Research Laboratory

world wide web
World Wide Web
  • WWW:
    • A global networkthat allows us to find, share, and combine information
    • A web of links
  • Information is represented using:
    • Natural Language (e.g., English)
    • Graphics, Multimedia..
  • “O.K.” for humans to comprehend
  • Difficult for machine processing
    • Ambiguity, Unconstrained data formats..

ISU Artificial Intelligence Research Laboratory

example searching
Example : Searching
  • Current search engines = keywords
    • Sensitive to syntax
    • Insensitive to semantics
    • High recall, low precision
  • Query: How many cows are there in Iowa?

= 1,234,567

ISU Artificial Intelligence Research Laboratory

example data integration
Example: Data Integration
  • Databases are different in terms of structure, content
  • Applications require managing several databases
    • After company mergers (e.g., K-Mart & Sears)
    • Biochemical, Genetics etc..
  • Semantics of the data(base) need to be specified explicitly (e.g., price & cost)

ISU Artificial Intelligence Research Laboratory

what is required
What is required ?
  • Ability for a resource to provide information about itself
    • Better known as “metadata”
    • E.g., Price – refers to price of an item without taxes
  • Ability to represent/store this information in machine-interpretable format
  • Ability to design vocabularies which would give well-defined meaning to the information
    • E.g., Pricemeans the same as Cost
  • Ability for agents to be able to reason about the (meta)data
    • E.g., if B brother of A, C brother of B => C brother of A
  • The solution : Semantic Web

ISU Artificial Intelligence Research Laboratory

the semantic web
The Semantic Web
  • A global network in which information is given well-defined meaning, better enabling computers and people to work in cooperation
    • Existing WWW is very human-oriented (E.g., Google)
  • A metadata based infrastructure for reasoning on the Web
    • Herbivorous animals eat grass, Cow is herbivorous..
  • Extends the current web, doesn’t replace it..!

ISU Artificial Intelligence Research Laboratory

semantic web layer cake
Semantic Web Layer Cake

ISU Artificial Intelligence Research Laboratory

outline9
Outline
  • Introduction & Motivation
  • XML
  • RDF & RDFS
  • DL
  • OWL
  • Future Look & Resources

ISU Artificial Intelligence Research Laboratory

road map
Road Map

We are here

ISU Artificial Intelligence Research Laboratory

slide11
XML
  • A text-based meta-language format for data exchange
  • Provides a pathway to transfer data easily between various applications
  • Markup or Tags – identifies structures in the document ( )
  • XML Schema – provides a schema to XML files
  • XML Query – a typed query language for XML documents

ISU Artificial Intelligence Research Laboratory

an example
An Example

Michael Jackson has a homepage http://www.michaeljackson.com and is the artist of album Bad

Michael Jackson has homepage

http://www.michaeljackson.com and is the artist of album

Bad

Michael Jacksonhttp://www.michaeljackson.comBad

ISU Artificial Intelligence Research Laboratory

xml file a labeled tree
XML file: a labeled tree

Structure or Syntax

artist

name

album

homepage

ISU Artificial Intelligence Research Laboratory

can xml provide semantics
Can XML provide Semantics ?

……

  • An un-manned aerial vehicle used by USAF for reconnaissance
  • An organism that lives by preying on other organisms
  • A company which specializes in manufacturing camouflage attire
  • A movie by the current Governor of California

ISU Artificial Intelligence Research Laboratory

limitations of xml
Limitations of XML
  • Makes no commitment towards domain-specific vocabulary
  • Interoperability (of meaning) feasible only for closed collaboration
    • agents in a small & stable community
    • pages on a small & stable intranet
  • Not suitable for sharing information in WWW

ISU Artificial Intelligence Research Laboratory

outline16
Outline
  • Introduction & Motivation
  • XML
  • RDF & RDFS
  • DL
  • OWL
  • Future Look & Resources

ISU Artificial Intelligence Research Laboratory

road map17
Road Map

We are here

ISU Artificial Intelligence Research Laboratory

what is resource description framework
What is Resource Description Framework ?
  • Defines a framework for structuring & describing resources (e.g., documents) in the Semantic Web
  • Enables the definition of vocabularies for the description of the resources
  • Goals:
    • Improved support for interpretation of data by machines
    • Extensibility, interoperability, and reuse of vocabularies

ISU Artificial Intelligence Research Laboratory

the rdf data model
The RDF Data Model
  • Simple but powerful model for creation of metadata
  • Can be expressed in XML
  • Consists of three concepts:
    • Resource: an element, a URI, a literal..
    • Properties : directed relations between two resources
    • Statements : triples of two resources bound by a property
      • Usual terminology: (s, p, o) subject, predicate, object

ISU Artificial Intelligence Research Laboratory

rdf statement graph
RDF Statement & Graph
  • Each triple (s, p, o) represents a RDF statement

Michael Jackson is the artist of Bad

subject

(resource)

predicate

(property)

object

(resource or literal)

http://www.michaeljackson.com

http://www.music.org/songs/mj/Bad

Artist

ISU Artificial Intelligence Research Laboratory

rdf resource
RDF Resource
  • The Resource forms the central concept in RDF
  • Anything can be described as a resource (E.g., website, book, picture, persons..)
  • Resources are identified by URI’s (plus the optional anchor ID’s)

http://www.music.org/songs/mj/Bad

http://www.michaeljackson.com

ISU Artificial Intelligence Research Laboratory

rdf property
RDF Property
  • Represents the predicate of an RDF statement
  • Is labeled with a URI referencing to a RDF property
  • Is directed pointing from the subject of a statement to the object of a statement

http://www.music.org/songs/mj/Bad

http://www.michaeljackson.com

Artist

music:Artist

ISU Artificial Intelligence Research Laboratory

representing rdf documents
Representing RDF documents
  • RDF Graph Syntax (abstract syntax)
  • Notation 3 and N-Triples
  • XML Syntax
    • XML Serialization Syntax
    • Abbreviated XML Syntax Variations

ISU Artificial Intelligence Research Laboratory

an example24
An Example
  • A person whose name is Michael Jackson and whose homepage is http://www.michaeljackson.com is the artist of http://www.music.org/songs/mj/Bad

ISU Artificial Intelligence Research Laboratory

how does rdf help
How does RDF help?
  • Vast majority of data processed by machines can be represented in the form of triples
  • Subject, Predicate, Object are identified by URI’s
    • Allows to uniquely identify them
    • Concepts are notjust words in a document, but are tied to a unique definition found in the Web
  • Uniqueness is vital to make a consistent statement
    • Michael Jackson denoted by http://www.michaeljackson.com means the same to everyone !

ISU Artificial Intelligence Research Laboratory

why is rdf not enough
Why is RDF not enough?
  • RDF properties can be regarded as attributes of resources
  • RDF properties also represent relationships between resources
  • But, RDF does not provide mechanisms for describing:
    • The properties (in terms of their range and domain)
    • The relationships between the properties and other resources

ISU Artificial Intelligence Research Laboratory

road map27
Road Map

We are here

ISU Artificial Intelligence Research Laboratory

rdf s rdf schema
RDF(S) – RDF Schema
  • The RDF Vocabulary Description Language
  • Enables us to :
    • Define classes of resources
    • Define relationships between the classes
    • Define the kinds of properties that instances of that classes have
    • Define relationships between properties

ISU Artificial Intelligence Research Laboratory

slide29

ISU Artificial Intelligence Research Laboratory

slide30
An Introduction to the

Semantic Web

Part 2: Description Logic and Web Ontology Language (OWL)

Jie Bao, AI Lab, Iowa State University

[email protected]

ISU Artificial Intelligence Research Laboratory

outline31
Outline
  • Introduction & Motivation
  • XML & RDF
  • RDFS
  • OWL
  • DL
  • Future look and Resources

ISU Artificial Intelligence Research Laboratory

slide32
Map

You are here

ISU Artificial Intelligence Research Laboratory

problems with rdfs
Problems with RDFS
  • RDFS too weak to describe resources in sufficient detail
    • No localised range and domain constraints
      • Can’t say that the range of hasChild is person when applied to persons and elephant when applied to elephants
    • No existence/cardinality constraints
      • Can’t say that all instances of Album have an artist that is also a person, or that albums have at least 1 artist
    • No transitive, inverse or symmetrical properties
      • Can’t say that isPartOf is a transitive property, that hasPart is the inverse of isPartOf or that touches is symmetrical
  • Difficult to provide reasoning support
    • No “native” reasoners for non-standard semantics
    • May be possible to reason via FO axiomatisation

ISU Artificial Intelligence Research Laboratory

problems with rdfs34
Problems with RDFS
  • RDFS is also too liberal
    • No distinction between classes and instances (individuals)

    • Properties can themselves have properties

    • No distinction between language constructors and ontology vocabulary, so constructors can be applied to themselves/each other

ISU Artificial Intelligence Research Laboratory

here comes the ontology
Here comes the ontology
  • The Tao that can be known is not Tao. The substance of the World is only a name for Tao. - Laozi, Tao Te Ching
  • Science of Being (Aristotle, Metaphysics, IV, 1)

ISU Artificial Intelligence Research Laboratory

ontology in computer science
Ontology in Computer Science
  • An ontology is an engineering artifact:
    • It is constituted by a specific vocabulary used to describe a certain reality, plus
    • a set of explicit assumptions regarding the intended meaning of the vocabulary.
  • Thus, an ontology describes a formal specification of a certain domain:
    • Shared understanding of a domain of interest
    • Formal and machine manipulable model of a domain of interest

“An explicit specification of a conceptualisation” [Gruber93]

ISU Artificial Intelligence Research Laboratory

web ontology language requirements
Web Ontology Language Requirements

Desirable features identified for Web Ontology Language:

  • Extends existing Web standards
    • Such as XML, RDF, RDFS
  • Easy to understand and use
    • Should be based on familiar KR idioms
  • Formally specified
  • Of “adequate” expressive power
  • Possible to provide automated reasoning support

ISU Artificial Intelligence Research Laboratory

web ontology languages
Web Ontology Languages
  • OIL (Ontology Interface Layer)
    • Outcome from “On-To-Knowledge” project sponsored by European IST (Information Society Technologies) project
  • DAML (DARPA Agent Markup Language)
    • Began as a DARPA research program
    • DAML-ONT
  • DAML+OIL
    • DAML combines OIL components
    • DAML-S, DAML-L
  • OWL (Web Ontology Language)
    • W3C standard
  • Future: SWRL,…

ISU Artificial Intelligence Research Laboratory

evolution of web ontology languages
Evolution of Web Ontology Languages

1992 1998 1999 2000 2001 2002 2003

Combine

vocabularies

OIL

Define vocabularies

Revision

XML

Extend

vocabularies

RDF

RDFS

DAML

(DAML+OIL)

OWL

DAML-ONT

SGML

For Web

services

OWL-S

Extend HTML tags

for semantic description

DAML-S

HTML

SHOE

ISU Artificial Intelligence Research Laboratory

slide40
OWL
  • Three species of OWL
    • OWL full is union of OWL syntax and RDF
    • OWL DL restricted to FOL fragment (¼ DAML+OIL)
    • OWL Lite is “easier to implement” subset of OWL DL
  • Semantic layering
    • OWL DL ¼ OWL full within DL fragment
    • DL semantics officially definitive
  • OWL DL based on SHIQDescription Logic
    • In fact it is equivalent to SHOIN(Dn) DL
  • OWL DL Benefits from many years of DL research
    • Well defined semantics
    • Formal properties well understood (complexity, decidability)
    • Known reasoning algorithms
    • Implemented systems (highly optimised)

ISU Artificial Intelligence Research Laboratory

owl language constructs
OWL Language Constructs
  • OWL Classes
    • Class descriptions
      • Enumeration
      • Property restriction:Value constraints , Cardinality constraints
      • Intersection, union and complement
    • Class axioms
  • OWL Properties
    • RDF Schema property constructs
    • Relations to other properties
    • Global cardinality restrictions on properties
    • Logical characteristics of properties
  • OWL Individuals
    • Individual identity
  • Datatypes
  • Annotations

ISU Artificial Intelligence Research Laboratory

owl class descriptions
OWL Class Descriptions
  • Enumeration

ISU Artificial Intelligence Research Laboratory

owl class descriptions43
OWL Class Descriptions
  • Property Restriction
    • Value constraints

ISU Artificial Intelligence Research Laboratory

owl class descriptions44
OWL Class Descriptions
  • Property Restriction
    • Cardinality constraints

2

2

2

ISU Artificial Intelligence Research Laboratory

owl class descriptions45
OWL Class Descriptions
  • Intersection

ISU Artificial Intelligence Research Laboratory

owl class descriptions46
OWL Class Descriptions
  • Union

ISU Artificial Intelligence Research Laboratory

owl class descriptions47
OWL Class Descriptions
  • Complement

ISU Artificial Intelligence Research Laboratory

owl class
OWL Class
  • Subclass

  • Equivalent Class

  • Disjoint

ISU Artificial Intelligence Research Laboratory

owl rdfs properties
OWL (RDFS) Properties
  • RDF Schema Property Constructs

  • Domain and Range

ISU Artificial Intelligence Research Laboratory

owl properties
OWL Properties
  • Relations to Other Properties
    • Equivalent property

    • Inverse

ISU Artificial Intelligence Research Laboratory

owl properties51
OWL Properties
  • Global cardinality constraints on properties
    • Functional property

    • Inverse functional property

ISU Artificial Intelligence Research Laboratory

owl properties52
OWL Properties
  • Logical characteristics of properties
    • Transitive property

    • Symmetric property

ISU Artificial Intelligence Research Laboratory

owl individual
OWL Individual
  • Same as

  • Different from

  • All different

ISU Artificial Intelligence Research Laboratory

datatypes
Datatypes
  • OWL uses RDF datatyping scheme
    • Referencing XML Schema datatypes
    • xsd:integer, xsd:decimal, xsd:double, xsd:string, xsd:boolean, xsd:nonNegativeInteger, xsd:negativeInteger, xsd:date, …
  • Enumerated datatype
    • Using Datarange construct
  • Datatype reasoning
    • As a minimum xsd:string and xsd:integer
    • For unsupported datatypes
      • Lexically identical literals: equal
      • Lexically different literals: would not be known to be either equal or unequal

ISU Artificial Intelligence Research Laboratory

annotations
Annotations
  • Ontology header
    • rdfs:label
    • rdfs:comment
    • rdfs:seeAlso
    • rdfs:isDefinedBy
    • owl:imports
  • Version information
    • owl:versionInfo
    • owl:priorVersion
    • owl:backwardCompatibleWith
    • owl:incompatibleWith
    • owl:DeprecatedClass and owl:DeprecatedProperty

ISU Artificial Intelligence Research Laboratory

syntax rdf xml
Syntax – RDF/XML

But XML is only an option. OWL could be independent to XML :

ISU Artificial Intelligence Research Laboratory

syntax n triple
Syntax – N-Triple
  • .
  • .
  • "Michael Jackson"^^ .
  • .
  • .
  • .
  • _:A8b489fX3aX10092fc6cf9X3aXX2dX7fea .
  • .
  • .
  • _:A8b489fX3aX10092fc6cf9X3aXX2dX7fea .
  • .
  • .
  • .
  • .
  • .
  • .

ISU Artificial Intelligence Research Laboratory

outline58
Outline
  • Introduction & Motivation
  • XML & RDF
  • RDFS
  • OWL
  • DL
  • Future look and Resources

ISU Artificial Intelligence Research Laboratory

slide59
Map

You are here

ISU Artificial Intelligence Research Laboratory

why logic
Why Logic
  • Inference, Inference, Inference
  • OWL can be used for inference
    • MJ is a RockMusic artist, so he is also a PopMusic artist.
  • But how?
    • With a formal representation that equalvalent to OWL, but more easy to do inference.
  • It’s logic

ISU Artificial Intelligence Research Laboratory

why description logic
Why Description Logic?
  • Formal semantics (typically model theoretic)
    • Decidable fragments of FOL
    • Closely related to Propositional Modal & Dynamic Logics
  • Provision of inference services
    • Sound and complete decision procedures for key problems
    • Implemented systems (highly optimised)

ISU Artificial Intelligence Research Laboratory

dl architecture
DL Architecture

Knowledge Base

Tbox (schema)

CD = Album u Music

GoodArtist ´ Artist u9 >=10hasAlbum CD u …

Interface

Inference System

Abox (data)

MJ : Artist

hMJ, Badi : hasAlbum

ISU Artificial Intelligence Research Laboratory

description logic family
Description Logic Family
  • DLs are a family of logic based KR formalisms
  • Particular languages mainly characterised by:
    • Set of constructors for building complex concepts and roles from simpler ones
    • Set of axioms for asserting facts about concepts, roles and individuals
  • ALC is the smallest DL that is propositionally closed
    • Constructors include booleans (and, or, not), and
    • Restrictions on role successors:

ISU Artificial Intelligence Research Laboratory

dl concept and role constructors
DL Concept and Role Constructors
  • Range of other constructors found in DLs, including:
    • Number restrictions (cardinality constraints) on roles, e.g., >=3 hasChild, =1 hasMother
    • Qualified number restrictions, e.g., >=2 hasChild.Female, =1 hasParent.Male
    • Nominals (singleton concepts), e.g., {Italy}
    • Concrete domains (datatypes), e.g., hasAge.(>=21), earns spends.<
    • Inverse roles, e.g., hasChild- (hasParent)
    • Transitive roles, e.g., hasChild* (descendant)

ISU Artificial Intelligence Research Laboratory

dl knowledge base
DL Knowledge Base
  • DL Knowledge Base (KB) normally separated into 2 parts:
    • TBox is a set of axioms describing structure of domain (i.e., a conceptual schema), e.g.:
      • HappyFather  Man ^hasChild.Female ^ …
      • Elephant < Animal ^ Large ^ Grey
      • transitive(ancestor)
    • ABox is a set of axioms describing a concrete situation (data), e.g.:
      • John:HappyFather
      • :hasChild
  • Separation has no logical significance
    • But may be conceptually and implementationally convenient

ISU Artificial Intelligence Research Laboratory

owl as dl class constructors
OWL as DL: Class Constructors

ISU Artificial Intelligence Research Laboratory

owl as dl axioms
OWL as DL: Axioms

ISU Artificial Intelligence Research Laboratory

owl dl semantics
OWL DL Semantics
  • Mapping OWL to equivalent DL (SHOIN(Dn)):
    • Facilitates provision of reasoning services (using DL systems)
    • Provides well defined semantics
  • DL semantics defined by interpretations: I = (DI, ¢I), where
    • DI is the domain (a non-empty set)
    • ¢I is an interpretation function that maps:
      • Concept (class) name A! subset AI of DI
      • Role (property) name R! binary relation RI over DI
      • Individual name i!iI element of DI

ISU Artificial Intelligence Research Laboratory

what the hell shoin d n is
What the hell SHOIN(Dn) is
  • S = ALCR+
  • ALC:
  • R+: transitive property
  • H: Role hierarchy
  • O: Nominal
  • I: Inverse role
  • N: unqualified number restriction, eg
  • Dn: n-ary datatype predicate.

ISU Artificial Intelligence Research Laboratory

dl semantics
DL Semantics

ISU Artificial Intelligence Research Laboratory

interpretation example
Interpretation Example

AI

= {v, w, x, y, z}

AI = {v, w, x}

BI = {x, y}

RI = {(v, w), (v, x), (y, x), (x, z)}

  • :B = {v,w,z}
  • A u B = {x}
  • :A t B = {y}
  • 9R B = {y,v}
  • 8R B = {y}
  • 61 R A = {y,z,x,w}
  • >1 R A = {v,y}

v

w

x

y

z

BI

ISU Artificial Intelligence Research Laboratory

inference tasks
Inference Tasks
  • Knowledge is correct (captures intuitions)
    • C subsumes D w.r.t. K iff for every modelI of K, CIµDI
  • Knowledge is minimally redundant (no unintended synonyms)
    • C is equivallent to D w.r.t. K iff for every modelI of K, CI = DI
  • Knowledge is meaningful (classes can have instances)
    • C is satisfiable w.r.t. K iff there exists some modelI of K s.t. CI;
  • Querying knowledge
    • x is an instance of C w.r.t. K iff for every modelI of K, xI2CI
    • hx,yi is an instance of R w.r.t. K iff for, every modelI of K, (xI,yI) 2RI
  • Knowledge base consistency
    • A KB K is consistent iff there exists some modelI of K

ISU Artificial Intelligence Research Laboratory

owl reasoners
OWL Reasoners
  • DL Reasoner
    • Using Tableaux algorithm
    • FaCT, Racer (written in Common LISP)
    • Decidability vs. Efficiency
  • Rule-based Reasoner
    • Using rule engine (Forward/Backward chaining)
    • Describing OWL semantics as a rule base
    • JESS (written in Java)
  • Logic-based Reasoner
    • Using theorem proving (Resolution), eg. Vampire
    • Describing OWL semantics as a logic program
    • JTP (written in Java)

ISU Artificial Intelligence Research Laboratory

outline74
Outline
  • Introduction & Motivation
  • XML & RDF
  • RDFS
  • DL
  • OWL
  • Future look and Resources

ISU Artificial Intelligence Research Laboratory

future
Future

And here, too

You will be here

ISU Artificial Intelligence Research Laboratory

slide76
Rule
  • antecedent ⇒ consequent
    • If A fatherOf B, A brotherOf C then C uncleOf B
    • father(?x,?y) ∧ brother(?y,?z) ⇒ uncle(?x,?z)
  • FOL, not DL, and is undecidable
  • Very active research topic this year
    • SWRL http://www.daml.org/2003/11/swrl/
    • RuleML http://www.ruleml.org/
    • [email protected] ISWC2004 http://2004.ruleml.org/

ISU Artificial Intelligence Research Laboratory

trust
Trust
  • If one person says that x is blue, and another says that x is not blue, doesn't the whole Semantic Web fall apart?
  • No!
    • Applications on the Semantic Web will depend on context,
    • Nonmonotonic reasoning can be applied.
    • We will have proof checking mechanisms, and digital signatures.
  • Proof Languages : a language that let's us prove whether or not a statement is true
    • Still missing at this moment

ISU Artificial Intelligence Research Laboratory

resource who s who
Resource – Who’s who

A very incomplete list, only some of whose work is related to us

James A. Hendler

Ian Horrocks

Natasha Noy

Deborah McGuinness

Frank van Harmelen

Tim Berners-Lee

Jeff Heflin

Dieter A. Fensel

ISU Artificial Intelligence Research Laboratory

resource who s who79
Resource – Who’s who

A very incomplete list, only some of whose work is related to us

Heiner

Stuckenschmidt's

Steffen Staab

Jérôme Euzenat

Ying Ding

Jeremy Carroll

Luciano Serafini

Paolo Bouquet

York Sure

ISU Artificial Intelligence Research Laboratory

resources projects
Resources - Projects
  • US
    • DAML: http://www.daml.org/
    • Mindswap : http://www.mindswap.org/
    • Protege: http://protege.stanford.edu/
    • HayStack: http://haystack.lcs.mit.edu/
  • International
    • RACER http://www.sts.tu-harburg.de/~r.f.moeller/racer/
  • Portal:
    • SemanticWeb: http://www.semanticweb.org
    • SemWebCentral: http://projects.semwebcentral.org/
    • SIGSEMIS: http://www.sigsemis.org/

ISU Artificial Intelligence Research Laboratory

resources projects81
Resources - Projects
  • EU
    • On-To-Knowledge http://www.ontoknowledge.org/ 99-02
    • OntoWeb: http://www.ontoweb.org/ 01-04
    • WonderWeb: http://wonderweb.semanticweb.org 02-04
    • Knowledge Web http://knowledgeweb.semanticweb.org/ 04-08
    • SWAD-Europe: http://www.w3.org/2001/sw/Europe/
    • SEKT: http://www.sekt-project.com/ 04-07
    • Sesame: http://www.openrdf.org/
    • KAON: http://kaon.semanticweb.org/
    • FaCT: http://www.cs.man.ac.uk/~horrocks/FaCT/
    • SWAP: http://swap.semanticweb.org/
    • SWWS: http://swws.semanticweb.org/

ISU Artificial Intelligence Research Laboratory

resources standards and de facto standards
Resources – Standards and de facto standards
  • XML: http://www.w3.org/XML/
  • RDF: http://www.w3.org/RDF/
  • OWL: http://www.w3.org/2001/sw/WebOnt/
  • DAML+OIL:http://www.daml.org/2001/03/daml+oil-index.html
  • RDQL:http://www.w3.org/Submission/RDQL/
  • SWRL: http://www.daml.org/2003/11/swrl/
  • Jena: http://jena.sourceforge.net/

ISU Artificial Intelligence Research Laboratory

resources conferences
Resources - Conferences
  • WWW: World Wide Web
  • ESWC: European Semantic Web Conference
  • DL: Description Logics
  • ISWC: International Semantic Web Conference
  • SWDB: Semantic Web and Databases
  • KR: Knowledge Representation and Reasoning
  • SWEB: Semantic Web Technologies in Electronic Business
  • Protege: Protege conference
  • DC: Dublin Core
  • WI: IEEE/WIC/ACM Web Intelligence
  • Semantic Technology Conference

http://www.w3.org/Search/Mail/Public/search?type-index=www-rdf-interest&index-type=t&keywords=CFP

http://groups.yahoo.com/group/semanticweb/messagesearch?query=cfp

ISU Artificial Intelligence Research Laboratory

questions
Questions

ISU Artificial Intelligence Research Laboratory

backup
backup

ISU Artificial Intelligence Research Laboratory

what information can we see
What information can we see…

WWW2002

The eleventh international world wide web conference

Sheraton waikiki hotel

Honolulu, hawaii, USA

7-11 may 2002

1 location 5 days learn interact

Registered participants coming from

australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire

Register now

On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event …

Speakers confirmed

Tim berners-lee

Tim is the well known inventor of the Web, …

Ian Foster

Ian is the pioneer of the Grid, the next generation internet …

ISU Artificial Intelligence Research Laboratory

what information can we see87
What information can we see…

ISU Artificial Intelligence Research Laboratory

what information can a machine see
What information can a machine see…

WWW2002

The eleventh international world wide web conference

Sheraton waikiki hotel

Honolulu, hawaii, USA

7-11 may 2002

1 location 5 days learn interact

Registered participants coming from

australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire

Register now

On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event …

Speakers confirmed

Tim berners-lee

Tim is the well known inventor of the Web, …

Ian Foster

Ian is the pioneer of the Grid, the next generation internet …

ISU Artificial Intelligence Research Laboratory

solution xml markup with meaningful tags
Solution: XML markup with “meaningful” tags?

WWW2002

The eleventh international world wide webcon

Sheraton waikiki hotel

Honolulu, hawaii, USA

7-11 may 2002

1 location 5 days learn interact

Registered participants coming from

australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire

Register now

On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event …

Speakers confirmed

Tim berners-lee

Tim is the well known inventor of the Web,

ISU Artificial Intelligence Research Laboratory

but what about
But What About…

WWW2002

The eleventh international world wide webcon

Sheraton waikiki hotel

Honolulu, hawaii, USA

7-11 may 2002

1 location 5 days learn interact

Registered participants coming from

australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire

Register now

On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event …

Speakers confirmed

Tim berners-lee

Tim is the well known inventor of the Web,…

ISU Artificial Intelligence Research Laboratory

machine sees
Machine sees…

WWW2002

The eleventh international world wide webc

Sheraton waikiki hotel

Honolulu, hawaii, USA

7-11 may 2002

1 location 5 days learn interact

Registered participants coming from

australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire

Register now

On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event …

Speakers confirmed

Tim berners-lee

Tim is the well known inventor of the W

Ian Foster

Ian is the pioneer of the Grid, the ne

ISU Artificial Intelligence Research Laboratory

ad