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Pragmatic Web 4.0
Kommission „Die Natur der Information“
Göttinger Akademie, 8.11.2013
Prof. Dr. Adrian Paschke
Corporate Semantic Web (AG-CSW)
Institut für Informatik, Freie Universität Berlin
paschke@inf.fu-berlin
http://www.inf.fu-berlin/groups/ag-csw/
and
Department of Information Systems
Poznan University of Economics
paschke@inf.fu-berlin
Agenda
 What is Semantics?
 Declarative Knowledge
Representation in IT
 The Semantic Web – An
Introduction
 Semantic Web and it’s Relations
 What comes next?
What is Semantics?
The Problem of Machine Meaning
Interpretation and Machine
Understanding
Data vs. Information
 Data
 A “given,” or fact; text, a number, a statement, or a
picture, …
 The raw materials in the production of information

 Information
 Data that has been put into a meaningful and useful
context.
Example Data vs. Information
data

95

information My score on the final exam is
95%
knowledge I have passed the exam with
excellent mark bdb

data
representation,
e.g. relational
DB

data + context
+ information
representation

data /
information +
meaning
interpretation
Search Results from Publication
Database
Title

 Lorenz P,
Transcriptional repression
mediated by the KRAB domain of the human
Author
C2H2 zinc finger protein Kox1/ZNF10 does not
require histone deacetylation.
Biol Chem. 2001 Apr;382(4):637-44.
 Fredericks WJ. An engineered PAX3-KRAB
transcriptional repressor inhibits the malignant
Journal
Year
phenotype of alveolar rhabdomyosarcoma
cells harboring the endogenous PAX3-FKHR
oncogene.
However, for a machine things look different!
Mol Cell Biol. 2000 Jul;20(14):5019-31.
Results from Publication Database
 Lorenz P, Transcriptional repression
mediated by the KRAB domain of the
human C2H2 zinc finger protein
Kox1/ZNF10 does not require histone
deacetylation.
Biol Chem. 2001 Apr;382(4):637-44.
 Fredericks WJ. An engineered PAX3KRAB transcriptional repressor inhibits
the malignant phenotype of alveolar
rhabdomyosarcoma cells harboring the
endogenous PAX3-FKHR oncogene.
Mol Cell Biol. 2000

Solution:
Tags (XML)?

Jul;20(14):5019-31.
Results from Publication Database
 <author>Lorenz P</author><title>Transcriptional repression
mediated by the KRAB domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require histone deacetylation.
</title>
<journal>Biol Chem </journal><year>2001<year>
 <author>Lorenz P</author><title>Transcriptional repression
mediated by the KRAB domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require histone deacetylation.
</title>
<journal>Biol Chem </journal><year>2001<year>
However, for a machine things look different!
 ...
Results from Publication Database
 <author>Lorenz
P</author><title>Transcriptional
repression mediated by the KRAB
domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require
histone deacetylation. </title>
<journal>Biol Chem
</journal><year>2001<year>
 <author>Lorenz
P</author><title>Transcriptional
repression mediated by the KRAB
domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require
histone deacetylation. </title>
<journal>Biol Chem
</journal><year>2001<year>

Solution: Use Semantic
Knowledge
Representation
Example: Traffic Light
Syntax – Semantics - Pragmatics

 Syntax
 green (bottom); yellow; red

 Semantics
 green = go; …; red = stop

 Pragmatics
 If red and no traffic
then allowed to go
Example: Question-Answer Interaction
Syntax – Semantics - Pragmatics
 Syntax
 “What time is it?” (English)

 Semantics
 Question about current time (Meaning)

 Pragmatics
 An answer to the question is obligatory
(even if time is unknown) (Understanding
and Commitment)
Example - XML Syntax vs. Semantics
Adrian Paschke is a lecturer of Logic Programming

<course name=“Logic Programming">
<lecturer>Adrian Paschke</lecturer>
</course>
<lecturer name=“Adrian Paschke">
<teaches>Logic Programming</teaches>
</lecturer>

Opposite nesting (syntax), same meaning (semantics)!
Syntax – Semantics - Pragmatics

 Syntax
 about form

 Semantics
 about meaning

 Pragmatics
 about use.
Information, Knowledge, Wisdom
Connectedness

Intelligence / Wisdom
understanding
principles

Pragmatics

Knowledge
Understanding
patterns

Sematics

Information / Content
Understanding relations
Data

Syntax
Understanding
Declarative
Knowledge
Representation

in IT
Semantic Technologies for
Declarative Knowledge Representation
1. Rules

 Describe derived conclusions if premium(Customer)
and reactions from given
then discount(10%)
information (inference)

2. Ontologies

equal
with

Customer



Ontologies represent the conceptual
knowledge of a domain (concept
semantics)

is a

Partner

Client
What is an Ontology? (in IT)
An Ontology is a

formal specification

 Executable, Discussable

of a shared

 Group of persons

conceptualization

 About concepts; abstract class

of a domain of interest

 e.g. an application, a specific
area, the “world model”

[Gruber 1993] - T.R. Gruber, Toward Principles for the Design of Ontologies Used for
Knowledge Sharing, Formal Analysis in Conceptual Analysis and Knowledge
Representation, Kluwer, 1993.
What is a Rule? (in IT)
1. Rules
•
•

Derivation rules (deduction rules): establish / derive new information
Reaction rules that establish when certain actions or effects should
take place :
• Condition-Action rules (production rules)
• Event-Condition-Action (ECA) rules + variants (e.g. ECAP).
• Messaging Reaction Rules (event message reaction rules)

2. Constraints
•
•
•

Structural constraints (e.g. deontic assignments).
Integrity constraints and state constraints
Process and flow constraints

[Paschke, A., Boley, H.]: Rule Markup Languages and Semantic Web Rule Languages, in Handbook of Research on Emerging Rule-Based
Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN:1-60566-402-2, 2009
Example: Ontology and Rules
Ontology

Object
is_a-1

Person
is_a-1

is_a-1

knows

has

Topic

described_in

Prior Art
Document

related_to

related_to

is_a-1

Patent
Application Priority

Skill

Patentee

Technique described_in
Teaching

writes

is_a-1

Patent

date

becomes

granted

RULES:
Topic
Patentee

writes

described_in
Patent
Application

Document
is_about

Topic

Topic
Patentee
Patentee

is_about
knows
has

Document
Topic
Skill
Ontologies and their relatives
informal

formal semantics

expressiveness
Based on AAAI’99 Ontologies Panel – McGuiness, Welty,
Ushold, Gruninger, Lehmann
Many Ontology Languages











No special ontolgy languages,
Entity Relationship Modell
but might be used to describe
ontologies
UML with OCL
Frames, F-Logic
Predicate Logic
Common Logic
formal logic
Description Logic
specialized
SHOE, XOL, OML, SKOS, OBO, SBVR, …
Web Ontology Languages
RDFS, DAML+OIL -> OWL
ODM
Ontology Transformation
…
Logic and Knowledge Representation
in IT
 Declarative Knowledge Representation
express what is valid, the responsibility to interpret
this and to decide on how to do it is delegated to an
automated interpreter / reasoner
 Formal logic-based languages for the
representation of knowledge with a clear
semantics
Main Requirements of a Logic-based
Ontology / Rule Language in IT
 a well-defined syntax
 a formal semantics
 sufficient expressive power
 efficient reasoning support
 convenience/adequacy of
expression syntax
Logic-based Knowledge Representation
 First Order Logic
 Expressive syntax: constants, functions, predicates, equality,
quantifiers, variables
 Objects and relations are semantic primitives represented as
predicate formula
 But: reasoning not efficient and undecidable

 Solution: Restriction to Subsets of FOL
 Horn Logic (Logic Programming / Rules)
 Descripition Logics (Ontologies)
 But: convenience of expression: only formal syntax +
semantics, but not a Web representation format
=> Semantic Web Syntax and Semantic Web Data Model needed
The Semantic Web
An Introduction and Overview
Semantic Web – An Introduction
 "The Semantic Web is an
extension of the current web in
which information is given welldefined meaning, better
enabling computers and people
to work in cooperation."
 Tim Berners-Lee, James Hendler,
Ora Lassila, The Semantic Web

 „Make the Web understandable
for machines“
W3C Stack 2007
Main Building Blocks of the
Semantic Web
1.
2.
3.
4.

Explicit Metadata on the WWW
Ontologies
Rule Logic and Inference
Semantic Tools ,Semantic Web Services,
Software Agents
The (current) W3C Semantic Web Stack

Ontologies

RDF Query
Language

Rules

Semantic Web
Information
Model

Standard
Internet
Technologies

W3C Semantic Web Stack since 2007
Overview on the Semantic Web
Technologies
 URI/IRI: Web Resource Identifiers
 Note: Representational State Transfer (REST) – Resources are
abstraction from an information/knowledge (e.g. „weather in Göttingen“)

 XML: Syntactic basis
 RDF: Resource Description Framework
 RDF as Web data model for facts and metadata
 RDF schema (RDFS) as simple ontology language
(mainly taxonomies)
 SPARQL as a RDF query language
 Linked Data – data publishing method
Overview on the Semantic Web
Technologies (2)
 Ontology
 RDF Schema (RDFS) and Web Ontology Language (OWL)

 Rules / Logic
 Rule Interchange Format (RIF, RuleML)

 Proof
 Generation of proofs-, interchange of proofs, validation

 Trust
 Digital signatures
 recommendations, ratings

 Semantic Web Applications & Interfaces
 e.g. Semantic Search, Semantic Agents, …
W3C Semantic Web (state: 2013)







IRIs + CURIE (Compact URI)
RDF 1.1, HTML+RDFa 1.1, RDB2RDF
SPARQL 1.1
RIF 1.0 (second edition)
OWL 2.0 (second edition)
Linked Open Data
 RDF 1.1, Turtel, JSON-LD 1.1, …

 Provenance
 Prov-DM, Prov-N, Prov-O, …
The (current) W3C Semantic Web
Architecture

W3C Semantic Web Stack since 2007
Example: RDF diagram
http://www.inf.fu-berlin.de/~adrianp/index.htm
dc:Creator
Adrian Paschke
Subject (= Ressource): http://www.inf.fu-berlin.de/~adrianp/index.htm
Predicate (= Property Attribute): dc:Creator
Object (= Value):
Adrian Paschke
resource-property-value triple = RDF triple = RDF statement
Read: <Ressource> has <Property> <Value>
Extended RDF Diagram
http://www.inf.fu-berlin.de/~adrianp/index.htm
c:Creator

http://www.inf.fu-berlin/Id/123
c:Name
Adrian Paschke

c:Email
adrian.paschke@inf.fu-berlin.de
RDF/XML-Version
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:s="http://description.org/schema/">
<rdf:Description about=" http://www.inf.fu-berlin.de/~adrianp/ ">
<s:Creator rdf:resource="http:// www.inf.fu-berlin.de/Id/123 "/>
</rdf:Description>
<rdf:Description about=" http:// www.inf.fu-berlin.de/Id/123 ">
<s:Name>Adrian Paschke</s:Name>
<s:Email>adrian.paschke@inf.fu-berlin.de</s:Email>
<rdf:Description>
</rdf:RDF>
RDF for Metadata Vocabulary
Example: Dublin Core in RDF
<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:dc="http://purl.org/dublin_core/schema/">
<rdf:Description rdf:about="responder.ruleml.org">
<dc:creator>A. Paschke</dc:creator>
<dc:title>Rule Responder</dc:title>
</rdf:Description>
</rdf:RDF>
Example: FOAF 0.1 – Metadata
Vocabulary (in RDF)
RDFa – RDF in HTML
Linked Open Data Cloud
Metadata Problem Domains
 Syntax:
 Which representation and interchange format for
metadata? (Microformats, RDF, RDFa, Microdata)
 Semantics:
 Which metadata are allowed for Web resources
(expressiveness, metadata vocabulary, schema)
 Association problem:
 How to connect metadata with resources? (who
defines the metadata, are metadata separated
from the content (RDF vs. RDFa), etc.)
The W3C Semantic Web
Architecture

W3C Semantic Web Stack since 2007
RDF Triple Stores
 A specialized database for RDF triples
 Supports a query language
 SPARQL is the W3C recommendation

 Triple stores might be in memory or provide a
persistent backend


Presistence provided by an underlying relational DBMS
(e.g., mySQL) or a custom DB for efficiency.
Example: SPARQL SELECT
 SELECT:
SELECT Variables
FROM Dataset
WHERE Pattern

 Examples:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
WHERE ( ?x foaf:name ?name )
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT *
WHERE ( ?x foaf:name ?name )
The (current) W3C Semantic Web
Architecture

W3C Semantic Web Stack since 2007
Example: RDFS Ontology
range

range

Literal

id

phone

domain

involves
domain

domain

Course

RDFS

range

subPropertyOf

isTaughtBy

Staff Member

domain

subClassOf

Academic Staff Member

range

subClassOf

subClassOf

subClassOf

Full
Professor

Associate
Professor

Assistant
Professor

rdf:type
rdf:type

RDF

isTaughtBy

Semantic Web

Adrian Paschke
RDF Schema Example
<rdf:RDF xml:lang=„en"
xmlns:rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs = "http://www.w3.org/2000/01/rdf-schema#">

<rdfs:Class rdf:ID="Professor">
<rdfs:comment>The class of full professors</rdfs:comment>
<rdfs:subClassOf rdf:resource=http://www.w3.org/2000/03/example/classes#AcademicStaffMember/>
</rdfs:Class>
<rdf:Property ID=„id">
<rdfs:range rdf:resource="http://www.w3.org/2000/03/example/classes#Integer" />
<rdfs:domain rdf:resource="#StaffMember" />
</rdf:Property>
<rdf:Property ID=„phone">
<rdfs:range rdf:resource="http://www.w3.org/2000/03/example/classes#Integer" />
<rdfs:domain rdf:resource = "#StaffMember" />
</rdf:Property>
…
</rfd:RDF>
OWL vs. RDFS
 More Expressiveness
 Definition of relations between classes
 Definition of constraints and cardinalities
 Constraints on properties: exists, forall, cardinality

 Definition of equivalences between classes (e.g.
different ontologies)
 Properties of properties
 Boolean combinations of classes and constraints
 …
Example: OWL Ontology
peopleAtUni

range

id

Student

involves
domain

domain

Course

OWL

Staff Member

range

domain

subClassOf
equivalentClass

subPropertyOf

isTaughtBy

phone

domain
unionOf

T-Box
Model

range

Literal

1

Faculty

Academic Staff Member
subClassOf

range
subClassOf

subClassOf
disjointWith

Professor

Assistant
Professor

Associate
Professor

rdf:type
rdf:type

RDF

isTaughtBy

Semantic Web

Adrian Paschke

A-Box
Model
Reasoning with OWL
 Semantics of OWL is defined by Description Logics (DL)
 Satisfiability: whether the assertions in an TBox and ABox has
a model (i.e. non-contradicting)
 Subsumption: whether one description is more general than
another one
 Equivalence: whether two classes denote same set
 Consistence: whether its set of assertions is consistent
 Instantiation: check if an individual is an instance of class C
 Retrieval: retrieve a set of individuals that instantiate C
The (current) W3C Semantic Web
Architecture

W3C Semantic Web Stack since 2007
Usage: Rule Interchange
Rules

Rules

serialize

de-serial.

Data model
(OWL, RDF-S,
XML-S, XMI, …)

Rule
system 1

Data

<RuleML doc>

serialize

Application A

<XML doc>
data

Rules

Rule
system 2

de-serial.

Data

Application B
Example: Rule Markup Language
Standards (RuleML)
 RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)
 Semantic Web Rule Language (SWRL)
 Uses RuleML Version 0.89

 Semantic Web Services Language (SWSL)
 Uses RuleML Version 0.89

 W3C Rule Interchange Format (RIF)
 Uses RuleML Version 0.91 with frames and slots

 OASIS LegalRuleML
 Uses RuleML Version 1.0

 OMG Production Rules Representation (PRR)
 Input from RuleML

 OMG Application Programming Interfaces four KBs (API4KB)
 Input from Reaction RuleML 1.0
Unifying Logic
• Not standardized in W3C Semantic Web Stack yet
• Which semantics? (e.g., Description Logics, F-Logic, Horn Logic, …)
• Which assumptions? (e.g., Closed World, Open World, Unique Name)
• …

W3C Semantic Web Stack since 2007
Example Decision Scenario
 You need to wait if the
traffic light is not green.
 I know that the traffic light
is green, so I’m allowed to
cross the street
 I’m not sure if the traffic
light is green, so I’m
allowed to cross the street
????
Open World vs. Closed Word
Assumption
 Open World Assumption (typical for ontologies)
 explicitly prove the truth of negation

 Closed World Assumption (typical for rules / logic programs)
 if we do not know (from our closed knowledge base) we assume
falsity

 This difference has practical implications
 Traffic light example:
 Under open world assumption we need to explicitly
prove that the light is not red => cross street
 Under closed world assumption it is enough if we
prove that there is no information if the light is red
=> cross street

 Who is responsible if an accident happens?
Unique-Names Assumption
isTaughtBy
domain

Course



range

1

Academic Staff Member

A course is taught by at most one staff member.
The course „Ontologies in IT“ is taught by
„Prof. Paschke“ and „Prof. Wecel“

OWL does not adopt the unique-names assumption
of database systems/logic programs (rules)


If two instances have a different name or keys/IDs does not
imply that they are different individuals

 An OWL reasoner does not flag an error


Instead it infers that the two resources are equal

 A rule reasoner / deductive database does flag an error
Proof and Trust
• Proof Markup Languages, Justifications and Argumentations
• Claims can be verified, if there are evidences from other (trusted) Internet
sources
• Semantic Reputation Models
Example Scenario – eCommerce
E-Shop

Review
Relying Party
Reseller Bob
Delivery
Service

Buyer

Monitored
Delivery
Performance

Business
Owner/Seller/Factory

used for service
management

used for buying
decisions
Semantic
Reputation Object

Semantic Web Reputation and Trust Management

http://www.corporate-semantic-web.de/rule-responder.html

Other Buyers
Use Cases / Applications / Tools












Semantic-enriched Search
Content management
Knowledge management
Business intelligence
Collaborative user interfaces
Sensor-based services
Linking virtual communities
Grid infrastructure
Multimedia data management
Semantic Web Services
Etc. see e.g.SWEO’s use case collection
http://www.w3.org/2001/sw/sweo/public/UseCases/
Semantic Search Engine

Gene Ontology
Example:
Semantic MediaWiki
Example:
What is located in California?
Example:
Semantic Desktop Systems
 Combine desktop systems with Semantic Web
Technologies
 Extract, manage, visualize and use semantic and
contextual associations respectively metadata for
Personal Information Management (PIM)


e.g. Gnowsis, Nepomuk, Beagle++, Social Semantic Desktop, Haystack
Example: Job Portal
Semantic Recommendation

d (Java, C++) = d (Java, Object Oriented) + d (C++, Object Oriented)
= (0.25-0.0.0625) + (0.25-0.0625)
= 0.375
sim(Java, C++) = 1 – 0.375 = 0.625 (Semantic Similarity is 0,625)
Example:
Query „Job offers for Java Programmer“ + expanded with Personal Skill Profile (Java +
C++ Knowledge)
=> also recommend job offers for C++ programmer
(see Semantic Matchmaking Framework: http://www.corporate-semantic-web.de/technologies.html
Finding Experts in Wikis
Example: Museum

DBPedia Deutschland
Semantic Wikipedia Germany

www.de.dbpedia.org
Semantic Annotation and Semantic Content Enrichment
The Semantic Web
and it‘s relations
Other Semantic
Standards/Specifications
Metadata

Terminology

Modeling

ISO/IEC 11179
Metadata
Registries
CONCEPT

Terminology
Thesaurus
Taxonomy
Ontology

Data
Standards

Logic

Graph RDF(S) / OWL

Metadata Registry

Structured
Metadata

Semantic
Web

Refers To

Referent

Symbolizes

“Rose”,
Stands For “ClipArt
Rose”

MOF
ODM
PRR
SBVR
API4KB
OntoIOP

Node

Subject

Edge

Predicate

ISO TC 37

OMG

F-Logic

RuleML
Common
Logic

Node

Object

SPARQL,RIF
ISO/IEC JTC 1/SC 32

FOL

W3C

Prolog
ISO,
RuleML,…
Example: OMG Ontology Definition Metamodel (ODM)

Ontology Definition Metamodel
MOF
MOF XMI
Of UML

MOF XMI
Of ODM

UML

ODM

User
UML Model

UML XMI
Of User Model

User
Ontology

Ontology XMI
Of User Model

ISO
Topic Maps

M2
M1

User
Instances
UML 2
(+OCL)

M3

M0
ISO
CL

W3C
RDFS

W3C
OWL

 ODM brings together the communities (SE+KR) by providing:
 Broad interoperation within Model Driven Architecture
 MDA tool access to ontology based reasoning capability
 UML notation for ontologies and ontological interpretation of UML
Example: Rule Markup Language
Standards (RuleML)
 RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)
 Semantic Web Rule Language (SWRL)
 Uses RuleML Version 0.89

 Semantic Web Services Language (SWSL)
 Uses RuleML Version 0.89

 W3C Rule Interchange Format (RIF)
 Uses RuleML Version 0.91 with frames and slots

 OASIS LegalRuleML
 Uses RuleML Version 1.0

 OMG Production Rules Representation (PRR)
 Input from RuleML

 OMG Application Programming Interfaces four KBs (API4KB)
 Input from Reaction RuleML 1.0
Social Semantic Web
The concept of the Social Semantic Web
subsumes developments in which social
interactions on the Web lead to the creation
of explicit and semantically rich knowledge
representations. (Wikipedia)
Corporate Semantic Web
Corporate Semantic Web (CSW) address
the applications of Semantic Web
technologies and Knowledge Management
methodologies in corporate environments
(semantic enterprises).
(www.corporate-semantic-web.de)
Corporate Semantic Web
Public Semantic Web

Corporate Semantic Web
Business Context
Corporate
Semantic
Engineering

Corporate
Semantic
Search

Corporate
Semantic
Collaboration

Corporate Business Information Systems
Pragmatic Web
 The Pragmatic Web consists of the tools,
practices and theories describing why and how
people use information. In contrast to the
Syntactic Web and Semantic Web the Pragmatic
Web is not only about form or meaning of
information, but about interaction which brings
about e.g. understanding or commitments.
(www.pragmaticweb.info)
Pragmatic Agent Web
The Pragmatic Agent Web utilize the Semantic Web with
multiple interacting intelligent agents which collaborate on
the Web and put independent meta data, ontologies and
local data into a pragmatic context such as communicative
situations, organizational norms, purposes or individual
goals and values.
Duration & Connectedness

Intelligence

Knowledge

Pragmatic

Semantic

Information
Syntax
Data
(Machine) Understanding
Pragmatic Agent Web (2)
 Utilize the heterogenous Semantic Web resources, meta data and
meaning representations with intelligent agents and web-based services
with the ability to understand the others intended meaning (pragmatic
competence)
 Formal Logic Representation vs. (Controlled) Natural Language Representation

 Collaborate in a communicative conversation-based process where
content and context is interchanged in terms of messages (relation of
signs) between senders and receivers (interpreters/users).
 Loosley-coupled vs. de-coupled interactions
 Fixed negotiation and coordination protocols vs. free conversations

 Pragmatic layer/wrapper around semantic/content e.g. by KQML / ACL
like speech-act primitives (e.g. assert(content), retract(content), query(kb))
 Model, negotiate and control shared and invividual meanings
 requires learning and knowledge adaption / updates
What comes next?
Challenges for the Semantic Web
Connectedness

Intelligence / Wisdom
understanding
principles

Pragmatics

Knowledge
Understanding
patterns

Sematics

Information / Content
Understanding relations

Data

Ontologies
(Logic)

Rules
(Logic)

Syntax
???

(Human Logic +
Machine Logic)

Understanding
Ubiquitous Pragmatic Web 4.0
Pragmatic Agent
Ecosystems

Machine
Understanding

Situation Aware Real-time Semantic
Complex Event Processing

Ubiquitous Pragmatic Web 4.0

Pragmatic Web

Connects Intelligent Agents and Smart Things

Massive
Multi-player Web Gaming

Ubiquitous autonomic
Smart Services and
Things

Smart Web TV

Social Semantic Web 3.0,
Web of Services & Things,
Corporate Semantic Web Connects

Semantic Web

Smart Content

People, Services and Things

Semantic Web 2.0
Connects Knowledge

Syntactic Web

World Wide Web 1.0

Smart Content

Passive

Active

Desktop Computing

Syntactic
Web

Semantic
Web

Consumer

Smart
Agents

XML
RDF

Monolithic
Systems Era

HTML

Desktop

Content
Producer

Connects Information

Pragmatic
Web

Ubiquitous Next Generation Agents and Social Connections
Thank you …

Questions?
http://www.corporate-semantic-web.de
http://www.pragmaticweb.info
AG Corporate Semantic Web, FU Berlin
paschke@inf.fu-berlin
http://www.inf.fu-berlin/groups/ag-csw/

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The Nature of Information

  • 1. Pragmatic Web 4.0 Kommission „Die Natur der Information“ Göttinger Akademie, 8.11.2013 Prof. Dr. Adrian Paschke Corporate Semantic Web (AG-CSW) Institut für Informatik, Freie Universität Berlin paschke@inf.fu-berlin http://www.inf.fu-berlin/groups/ag-csw/ and Department of Information Systems Poznan University of Economics paschke@inf.fu-berlin
  • 2. Agenda  What is Semantics?  Declarative Knowledge Representation in IT  The Semantic Web – An Introduction  Semantic Web and it’s Relations  What comes next?
  • 3. What is Semantics? The Problem of Machine Meaning Interpretation and Machine Understanding
  • 4. Data vs. Information  Data  A “given,” or fact; text, a number, a statement, or a picture, …  The raw materials in the production of information  Information  Data that has been put into a meaningful and useful context.
  • 5. Example Data vs. Information data 95 information My score on the final exam is 95% knowledge I have passed the exam with excellent mark bdb data representation, e.g. relational DB data + context + information representation data / information + meaning interpretation
  • 6. Search Results from Publication Database Title  Lorenz P, Transcriptional repression mediated by the KRAB domain of the human Author C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637-44.  Fredericks WJ. An engineered PAX3-KRAB transcriptional repressor inhibits the malignant Journal Year phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. However, for a machine things look different! Mol Cell Biol. 2000 Jul;20(14):5019-31.
  • 7. Results from Publication Database  Lorenz P, Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637-44.  Fredericks WJ. An engineered PAX3KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Solution: Tags (XML)? Jul;20(14):5019-31.
  • 8. Results from Publication Database  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year>  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year> However, for a machine things look different!  ...
  • 9. Results from Publication Database  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year>  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year> Solution: Use Semantic Knowledge Representation
  • 10. Example: Traffic Light Syntax – Semantics - Pragmatics  Syntax  green (bottom); yellow; red  Semantics  green = go; …; red = stop  Pragmatics  If red and no traffic then allowed to go
  • 11. Example: Question-Answer Interaction Syntax – Semantics - Pragmatics  Syntax  “What time is it?” (English)  Semantics  Question about current time (Meaning)  Pragmatics  An answer to the question is obligatory (even if time is unknown) (Understanding and Commitment)
  • 12. Example - XML Syntax vs. Semantics Adrian Paschke is a lecturer of Logic Programming <course name=“Logic Programming"> <lecturer>Adrian Paschke</lecturer> </course> <lecturer name=“Adrian Paschke"> <teaches>Logic Programming</teaches> </lecturer> Opposite nesting (syntax), same meaning (semantics)!
  • 13. Syntax – Semantics - Pragmatics  Syntax  about form  Semantics  about meaning  Pragmatics  about use.
  • 14. Information, Knowledge, Wisdom Connectedness Intelligence / Wisdom understanding principles Pragmatics Knowledge Understanding patterns Sematics Information / Content Understanding relations Data Syntax Understanding
  • 16. Semantic Technologies for Declarative Knowledge Representation 1. Rules  Describe derived conclusions if premium(Customer) and reactions from given then discount(10%) information (inference) 2. Ontologies equal with Customer  Ontologies represent the conceptual knowledge of a domain (concept semantics) is a Partner Client
  • 17. What is an Ontology? (in IT) An Ontology is a formal specification  Executable, Discussable of a shared  Group of persons conceptualization  About concepts; abstract class of a domain of interest  e.g. an application, a specific area, the “world model” [Gruber 1993] - T.R. Gruber, Toward Principles for the Design of Ontologies Used for Knowledge Sharing, Formal Analysis in Conceptual Analysis and Knowledge Representation, Kluwer, 1993.
  • 18. What is a Rule? (in IT) 1. Rules • • Derivation rules (deduction rules): establish / derive new information Reaction rules that establish when certain actions or effects should take place : • Condition-Action rules (production rules) • Event-Condition-Action (ECA) rules + variants (e.g. ECAP). • Messaging Reaction Rules (event message reaction rules) 2. Constraints • • • Structural constraints (e.g. deontic assignments). Integrity constraints and state constraints Process and flow constraints [Paschke, A., Boley, H.]: Rule Markup Languages and Semantic Web Rule Languages, in Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN:1-60566-402-2, 2009
  • 19. Example: Ontology and Rules Ontology Object is_a-1 Person is_a-1 is_a-1 knows has Topic described_in Prior Art Document related_to related_to is_a-1 Patent Application Priority Skill Patentee Technique described_in Teaching writes is_a-1 Patent date becomes granted RULES: Topic Patentee writes described_in Patent Application Document is_about Topic Topic Patentee Patentee is_about knows has Document Topic Skill
  • 20. Ontologies and their relatives informal formal semantics expressiveness Based on AAAI’99 Ontologies Panel – McGuiness, Welty, Ushold, Gruninger, Lehmann
  • 21. Many Ontology Languages           No special ontolgy languages, Entity Relationship Modell but might be used to describe ontologies UML with OCL Frames, F-Logic Predicate Logic Common Logic formal logic Description Logic specialized SHOE, XOL, OML, SKOS, OBO, SBVR, … Web Ontology Languages RDFS, DAML+OIL -> OWL ODM Ontology Transformation …
  • 22. Logic and Knowledge Representation in IT  Declarative Knowledge Representation express what is valid, the responsibility to interpret this and to decide on how to do it is delegated to an automated interpreter / reasoner  Formal logic-based languages for the representation of knowledge with a clear semantics
  • 23. Main Requirements of a Logic-based Ontology / Rule Language in IT  a well-defined syntax  a formal semantics  sufficient expressive power  efficient reasoning support  convenience/adequacy of expression syntax
  • 24. Logic-based Knowledge Representation  First Order Logic  Expressive syntax: constants, functions, predicates, equality, quantifiers, variables  Objects and relations are semantic primitives represented as predicate formula  But: reasoning not efficient and undecidable  Solution: Restriction to Subsets of FOL  Horn Logic (Logic Programming / Rules)  Descripition Logics (Ontologies)  But: convenience of expression: only formal syntax + semantics, but not a Web representation format => Semantic Web Syntax and Semantic Web Data Model needed
  • 25. The Semantic Web An Introduction and Overview
  • 26. Semantic Web – An Introduction  "The Semantic Web is an extension of the current web in which information is given welldefined meaning, better enabling computers and people to work in cooperation."  Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web  „Make the Web understandable for machines“ W3C Stack 2007
  • 27. Main Building Blocks of the Semantic Web 1. 2. 3. 4. Explicit Metadata on the WWW Ontologies Rule Logic and Inference Semantic Tools ,Semantic Web Services, Software Agents
  • 28. The (current) W3C Semantic Web Stack Ontologies RDF Query Language Rules Semantic Web Information Model Standard Internet Technologies W3C Semantic Web Stack since 2007
  • 29. Overview on the Semantic Web Technologies  URI/IRI: Web Resource Identifiers  Note: Representational State Transfer (REST) – Resources are abstraction from an information/knowledge (e.g. „weather in Göttingen“)  XML: Syntactic basis  RDF: Resource Description Framework  RDF as Web data model for facts and metadata  RDF schema (RDFS) as simple ontology language (mainly taxonomies)  SPARQL as a RDF query language  Linked Data – data publishing method
  • 30. Overview on the Semantic Web Technologies (2)  Ontology  RDF Schema (RDFS) and Web Ontology Language (OWL)  Rules / Logic  Rule Interchange Format (RIF, RuleML)  Proof  Generation of proofs-, interchange of proofs, validation  Trust  Digital signatures  recommendations, ratings  Semantic Web Applications & Interfaces  e.g. Semantic Search, Semantic Agents, …
  • 31. W3C Semantic Web (state: 2013)       IRIs + CURIE (Compact URI) RDF 1.1, HTML+RDFa 1.1, RDB2RDF SPARQL 1.1 RIF 1.0 (second edition) OWL 2.0 (second edition) Linked Open Data  RDF 1.1, Turtel, JSON-LD 1.1, …  Provenance  Prov-DM, Prov-N, Prov-O, …
  • 32. The (current) W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 33. Example: RDF diagram http://www.inf.fu-berlin.de/~adrianp/index.htm dc:Creator Adrian Paschke Subject (= Ressource): http://www.inf.fu-berlin.de/~adrianp/index.htm Predicate (= Property Attribute): dc:Creator Object (= Value): Adrian Paschke resource-property-value triple = RDF triple = RDF statement Read: <Ressource> has <Property> <Value>
  • 35. RDF/XML-Version <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:s="http://description.org/schema/"> <rdf:Description about=" http://www.inf.fu-berlin.de/~adrianp/ "> <s:Creator rdf:resource="http:// www.inf.fu-berlin.de/Id/123 "/> </rdf:Description> <rdf:Description about=" http:// www.inf.fu-berlin.de/Id/123 "> <s:Name>Adrian Paschke</s:Name> <s:Email>adrian.paschke@inf.fu-berlin.de</s:Email> <rdf:Description> </rdf:RDF>
  • 36. RDF for Metadata Vocabulary Example: Dublin Core in RDF <?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dublin_core/schema/"> <rdf:Description rdf:about="responder.ruleml.org"> <dc:creator>A. Paschke</dc:creator> <dc:title>Rule Responder</dc:title> </rdf:Description> </rdf:RDF>
  • 37. Example: FOAF 0.1 – Metadata Vocabulary (in RDF)
  • 38. RDFa – RDF in HTML
  • 40. Metadata Problem Domains  Syntax:  Which representation and interchange format for metadata? (Microformats, RDF, RDFa, Microdata)  Semantics:  Which metadata are allowed for Web resources (expressiveness, metadata vocabulary, schema)  Association problem:  How to connect metadata with resources? (who defines the metadata, are metadata separated from the content (RDF vs. RDFa), etc.)
  • 41. The W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 42. RDF Triple Stores  A specialized database for RDF triples  Supports a query language  SPARQL is the W3C recommendation  Triple stores might be in memory or provide a persistent backend  Presistence provided by an underlying relational DBMS (e.g., mySQL) or a custom DB for efficiency.
  • 43. Example: SPARQL SELECT  SELECT: SELECT Variables FROM Dataset WHERE Pattern  Examples: PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name WHERE ( ?x foaf:name ?name ) PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT * WHERE ( ?x foaf:name ?name )
  • 44. The (current) W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 45. Example: RDFS Ontology range range Literal id phone domain involves domain domain Course RDFS range subPropertyOf isTaughtBy Staff Member domain subClassOf Academic Staff Member range subClassOf subClassOf subClassOf Full Professor Associate Professor Assistant Professor rdf:type rdf:type RDF isTaughtBy Semantic Web Adrian Paschke
  • 46. RDF Schema Example <rdf:RDF xml:lang=„en" xmlns:rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs = "http://www.w3.org/2000/01/rdf-schema#"> <rdfs:Class rdf:ID="Professor"> <rdfs:comment>The class of full professors</rdfs:comment> <rdfs:subClassOf rdf:resource=http://www.w3.org/2000/03/example/classes#AcademicStaffMember/> </rdfs:Class> <rdf:Property ID=„id"> <rdfs:range rdf:resource="http://www.w3.org/2000/03/example/classes#Integer" /> <rdfs:domain rdf:resource="#StaffMember" /> </rdf:Property> <rdf:Property ID=„phone"> <rdfs:range rdf:resource="http://www.w3.org/2000/03/example/classes#Integer" /> <rdfs:domain rdf:resource = "#StaffMember" /> </rdf:Property> … </rfd:RDF>
  • 47. OWL vs. RDFS  More Expressiveness  Definition of relations between classes  Definition of constraints and cardinalities  Constraints on properties: exists, forall, cardinality  Definition of equivalences between classes (e.g. different ontologies)  Properties of properties  Boolean combinations of classes and constraints  …
  • 48. Example: OWL Ontology peopleAtUni range id Student involves domain domain Course OWL Staff Member range domain subClassOf equivalentClass subPropertyOf isTaughtBy phone domain unionOf T-Box Model range Literal 1 Faculty Academic Staff Member subClassOf range subClassOf subClassOf disjointWith Professor Assistant Professor Associate Professor rdf:type rdf:type RDF isTaughtBy Semantic Web Adrian Paschke A-Box Model
  • 49. Reasoning with OWL  Semantics of OWL is defined by Description Logics (DL)  Satisfiability: whether the assertions in an TBox and ABox has a model (i.e. non-contradicting)  Subsumption: whether one description is more general than another one  Equivalence: whether two classes denote same set  Consistence: whether its set of assertions is consistent  Instantiation: check if an individual is an instance of class C  Retrieval: retrieve a set of individuals that instantiate C
  • 50. The (current) W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 51. Usage: Rule Interchange Rules Rules serialize de-serial. Data model (OWL, RDF-S, XML-S, XMI, …) Rule system 1 Data <RuleML doc> serialize Application A <XML doc> data Rules Rule system 2 de-serial. Data Application B
  • 52. Example: Rule Markup Language Standards (RuleML)  RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)  Semantic Web Rule Language (SWRL)  Uses RuleML Version 0.89  Semantic Web Services Language (SWSL)  Uses RuleML Version 0.89  W3C Rule Interchange Format (RIF)  Uses RuleML Version 0.91 with frames and slots  OASIS LegalRuleML  Uses RuleML Version 1.0  OMG Production Rules Representation (PRR)  Input from RuleML  OMG Application Programming Interfaces four KBs (API4KB)  Input from Reaction RuleML 1.0
  • 53. Unifying Logic • Not standardized in W3C Semantic Web Stack yet • Which semantics? (e.g., Description Logics, F-Logic, Horn Logic, …) • Which assumptions? (e.g., Closed World, Open World, Unique Name) • … W3C Semantic Web Stack since 2007
  • 54. Example Decision Scenario  You need to wait if the traffic light is not green.  I know that the traffic light is green, so I’m allowed to cross the street  I’m not sure if the traffic light is green, so I’m allowed to cross the street ????
  • 55. Open World vs. Closed Word Assumption  Open World Assumption (typical for ontologies)  explicitly prove the truth of negation  Closed World Assumption (typical for rules / logic programs)  if we do not know (from our closed knowledge base) we assume falsity  This difference has practical implications  Traffic light example:  Under open world assumption we need to explicitly prove that the light is not red => cross street  Under closed world assumption it is enough if we prove that there is no information if the light is red => cross street  Who is responsible if an accident happens?
  • 56. Unique-Names Assumption isTaughtBy domain Course  range 1 Academic Staff Member A course is taught by at most one staff member. The course „Ontologies in IT“ is taught by „Prof. Paschke“ and „Prof. Wecel“ OWL does not adopt the unique-names assumption of database systems/logic programs (rules)  If two instances have a different name or keys/IDs does not imply that they are different individuals  An OWL reasoner does not flag an error  Instead it infers that the two resources are equal  A rule reasoner / deductive database does flag an error
  • 57. Proof and Trust • Proof Markup Languages, Justifications and Argumentations • Claims can be verified, if there are evidences from other (trusted) Internet sources • Semantic Reputation Models
  • 58. Example Scenario – eCommerce E-Shop Review Relying Party Reseller Bob Delivery Service Buyer Monitored Delivery Performance Business Owner/Seller/Factory used for service management used for buying decisions Semantic Reputation Object Semantic Web Reputation and Trust Management http://www.corporate-semantic-web.de/rule-responder.html Other Buyers
  • 59. Use Cases / Applications / Tools            Semantic-enriched Search Content management Knowledge management Business intelligence Collaborative user interfaces Sensor-based services Linking virtual communities Grid infrastructure Multimedia data management Semantic Web Services Etc. see e.g.SWEO’s use case collection http://www.w3.org/2001/sw/sweo/public/UseCases/
  • 62. Example: What is located in California?
  • 63. Example: Semantic Desktop Systems  Combine desktop systems with Semantic Web Technologies  Extract, manage, visualize and use semantic and contextual associations respectively metadata for Personal Information Management (PIM)  e.g. Gnowsis, Nepomuk, Beagle++, Social Semantic Desktop, Haystack
  • 64. Example: Job Portal Semantic Recommendation d (Java, C++) = d (Java, Object Oriented) + d (C++, Object Oriented) = (0.25-0.0.0625) + (0.25-0.0625) = 0.375 sim(Java, C++) = 1 – 0.375 = 0.625 (Semantic Similarity is 0,625) Example: Query „Job offers for Java Programmer“ + expanded with Personal Skill Profile (Java + C++ Knowledge) => also recommend job offers for C++ programmer (see Semantic Matchmaking Framework: http://www.corporate-semantic-web.de/technologies.html
  • 66. Example: Museum DBPedia Deutschland Semantic Wikipedia Germany www.de.dbpedia.org Semantic Annotation and Semantic Content Enrichment
  • 67. The Semantic Web and it‘s relations
  • 68. Other Semantic Standards/Specifications Metadata Terminology Modeling ISO/IEC 11179 Metadata Registries CONCEPT Terminology Thesaurus Taxonomy Ontology Data Standards Logic Graph RDF(S) / OWL Metadata Registry Structured Metadata Semantic Web Refers To Referent Symbolizes “Rose”, Stands For “ClipArt Rose” MOF ODM PRR SBVR API4KB OntoIOP Node Subject Edge Predicate ISO TC 37 OMG F-Logic RuleML Common Logic Node Object SPARQL,RIF ISO/IEC JTC 1/SC 32 FOL W3C Prolog ISO, RuleML,…
  • 69. Example: OMG Ontology Definition Metamodel (ODM) Ontology Definition Metamodel MOF MOF XMI Of UML MOF XMI Of ODM UML ODM User UML Model UML XMI Of User Model User Ontology Ontology XMI Of User Model ISO Topic Maps M2 M1 User Instances UML 2 (+OCL) M3 M0 ISO CL W3C RDFS W3C OWL  ODM brings together the communities (SE+KR) by providing:  Broad interoperation within Model Driven Architecture  MDA tool access to ontology based reasoning capability  UML notation for ontologies and ontological interpretation of UML
  • 70. Example: Rule Markup Language Standards (RuleML)  RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)  Semantic Web Rule Language (SWRL)  Uses RuleML Version 0.89  Semantic Web Services Language (SWSL)  Uses RuleML Version 0.89  W3C Rule Interchange Format (RIF)  Uses RuleML Version 0.91 with frames and slots  OASIS LegalRuleML  Uses RuleML Version 1.0  OMG Production Rules Representation (PRR)  Input from RuleML  OMG Application Programming Interfaces four KBs (API4KB)  Input from Reaction RuleML 1.0
  • 71. Social Semantic Web The concept of the Social Semantic Web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. (Wikipedia)
  • 72. Corporate Semantic Web Corporate Semantic Web (CSW) address the applications of Semantic Web technologies and Knowledge Management methodologies in corporate environments (semantic enterprises). (www.corporate-semantic-web.de)
  • 73. Corporate Semantic Web Public Semantic Web Corporate Semantic Web Business Context Corporate Semantic Engineering Corporate Semantic Search Corporate Semantic Collaboration Corporate Business Information Systems
  • 74. Pragmatic Web  The Pragmatic Web consists of the tools, practices and theories describing why and how people use information. In contrast to the Syntactic Web and Semantic Web the Pragmatic Web is not only about form or meaning of information, but about interaction which brings about e.g. understanding or commitments. (www.pragmaticweb.info)
  • 75. Pragmatic Agent Web The Pragmatic Agent Web utilize the Semantic Web with multiple interacting intelligent agents which collaborate on the Web and put independent meta data, ontologies and local data into a pragmatic context such as communicative situations, organizational norms, purposes or individual goals and values. Duration & Connectedness Intelligence Knowledge Pragmatic Semantic Information Syntax Data (Machine) Understanding
  • 76. Pragmatic Agent Web (2)  Utilize the heterogenous Semantic Web resources, meta data and meaning representations with intelligent agents and web-based services with the ability to understand the others intended meaning (pragmatic competence)  Formal Logic Representation vs. (Controlled) Natural Language Representation  Collaborate in a communicative conversation-based process where content and context is interchanged in terms of messages (relation of signs) between senders and receivers (interpreters/users).  Loosley-coupled vs. de-coupled interactions  Fixed negotiation and coordination protocols vs. free conversations  Pragmatic layer/wrapper around semantic/content e.g. by KQML / ACL like speech-act primitives (e.g. assert(content), retract(content), query(kb))  Model, negotiate and control shared and invividual meanings  requires learning and knowledge adaption / updates
  • 78. Challenges for the Semantic Web Connectedness Intelligence / Wisdom understanding principles Pragmatics Knowledge Understanding patterns Sematics Information / Content Understanding relations Data Ontologies (Logic) Rules (Logic) Syntax ??? (Human Logic + Machine Logic) Understanding
  • 79. Ubiquitous Pragmatic Web 4.0 Pragmatic Agent Ecosystems Machine Understanding Situation Aware Real-time Semantic Complex Event Processing Ubiquitous Pragmatic Web 4.0 Pragmatic Web Connects Intelligent Agents and Smart Things Massive Multi-player Web Gaming Ubiquitous autonomic Smart Services and Things Smart Web TV Social Semantic Web 3.0, Web of Services & Things, Corporate Semantic Web Connects Semantic Web Smart Content People, Services and Things Semantic Web 2.0 Connects Knowledge Syntactic Web World Wide Web 1.0 Smart Content Passive Active Desktop Computing Syntactic Web Semantic Web Consumer Smart Agents XML RDF Monolithic Systems Era HTML Desktop Content Producer Connects Information Pragmatic Web Ubiquitous Next Generation Agents and Social Connections
  • 80. Thank you … Questions? http://www.corporate-semantic-web.de http://www.pragmaticweb.info AG Corporate Semantic Web, FU Berlin paschke@inf.fu-berlin http://www.inf.fu-berlin/groups/ag-csw/