Semantic Web and Knowledge Management

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Semantic Web and Knowledge Management

• Ching-Long Yeh ???
• Department of Computer Science and Engineering
• Tatung University
• Taipei, Taiwan
• chingyeh_at_cse.ttu.edu.tw (msn)
• http//www.cse.ttu.edu.tw/chingyeh

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Content

• Introduction
• WWW HTML, HTTP, browsers
• XML and its Protocol
• Business Automation RosettaNet, ebXML
• Semantic Web WWW metadata layer
• Semantic Grid
• Semantic Web
• Overview
• Reasoning in Prolog
• Languages RDF, RDFS, OWL, OWL-S, SWRL, SPARQL
• Ontologies
• RSS, FOAF, iCalendar, vCard, DC(Q), musicBrainz
• Semantic Web System Architecture
• Knowledge-Engineering Approach to Knowledge
  Management
• KE methodology CommonKADS
• Our Current Research
• Lesson Learned in Project Management Based on
  Semantic Web
• From Text to RDF
• Summary

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Web Technology Overview

• WWW
• Infrastructure
• HTML, HTTP, URI, browsers
• Services
• Search engine and directory navigation
• WWW XML
• Web Service (UDDI, WSDL, SOAP)
• SOA (Registry, provider, requester)
• ebXML
• SOA for business automation
• discovery, implementation, run-time phases
• Business process message service
• Semantic Web
• Meaning processing automation
• WWW metadata layer (OWLRDF)
• Services automation (WWWOWL-S/RDF)
• Semantic Grid

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Semantic Web
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Semantic Web

• The Semantic Web is a vision

the idea of having data on the web defined and
linked in a way that it can be used by machines
not just for display purposes, but for
automation, integration and reuse of data across
various applications
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Semantic Web

• The Semantic Web a Web with a meaning.

"If HTML and the Web made all the online
documents look like one huge book, RDF, schema,
and inference languages will make all the data in
the world look like one huge database Tim
Berners-Lee, Weaving the Web, 1999
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Introduction from W3C SW Activity

• The Semantic Web is a web of data.
• The Semantic Web is about two things.
• Common formats for interchange of data,
• On the original Web we only had interchange of
  documents.
• Language for recording how the data relates to
  real world objects
• That allows a person, or a machine, to start off
  in one database, and then move through an
  unending set of databases which are connected not
  by wires but by being about the same thing.

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The Semantic Web Architecture
Trust
Sig./ Ency.
Proof
Tim Berners-Lee Axioms, Architecture and
Aspirations W3C all-working group plenary
Meeting 28 February 2001
Logic (FOL)
Rules (SWRL)
(http//www.w3.org/2001/Talks/0228-tbl/slide5-0.ht
ml)
Ontology (OWL)
RDF Schema
I. Horrocks, et al. Semantic web architecture
Stack or two towers? In F. Fages and S. Soliman,
(eds.), Principles and Practice of Semantic Web
Reasoning (PPSWR 2005), number 3703 in LNCS,
pages 37-41. SV, 2005. http//www.cs.man.ac.uk/h
orrocks/Publications/download/2005/HPPH05.pdf
RDF MS
XML Schema
XML
Namespaces
URI
Unicode
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Reasoning in Prolog (1)

• Facts and rules about members of a family

parent(tom,bob). parent(pam,bob). parent(tom,bob).
parent(tom,liz). parent(bob,ann). parent(bob,pat)
. parent(pat,jim). female(pam). male(tom). male(bo
b). female(liz). female(pat). female(ann). male(ji
m).
offspring(Y,X)- parent(X,Y). mother(X,Y)-
parent(X,Y),female(X). grandparent(X,Z)-
parent(X,Y),parent(Y,Z). sister(X,Y)-
parent(Z,X),parent(Z,Y),female(X),
X\Y. predecessor(X,Z)- parent(X,Z). predeces
sor(X,Z)- parent(X,Y), predecessor(Y,Z).
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Reasoning in Prolog (2)

• The following unlisted facts can be derived by
  using the rules.

offspring(bob,pam). offspring(bob,tom). offspring(
liz,tom). offspring(ann,bob). offspring(pat,bob).
offspring(jim,pat). mother(pam,bob). mother(pat,j
im). grandparent(tom,ann). grandparent(tom,pat).
grandparent(pam,ann). grandparent(pam,pat). grandp
arent(tom,ann). grandparent(tom,pat). grandparent(
bob,jim).
sister(liz,bob). sister(ann,pat). sister(pat,ann).
predecessor(pam,bob). predecessor(tom,bob). pred
ecessor(tom,liz). predecessor(bob,ann). predecesso
r(bob,pat). predecessor(pat,jim). predecessor(pam,
ann). predecessor(pam,pat). predecessor(pam,jim).
predecessor(tom,ann). predecessor(tom,pat). predec
essor(tom,jim). predecessor(bob,jim).
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RDF and Schema Languages
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RDF MS

• RDF (Resource Description Framework)
• Beyond Machine readable to Machine understandable
• RDF consists of two parts
• RDF Model (a set of triples)
• RDF Syntax (different XML serialization syntaxes)
• RDF Schema for definition of Vocabularies (simple
  Ontologies) for RDF (and in RDF)

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RDF Data Model

• Resources
• A resource is a thing you talk about (can
  reference)
• Resources have URIs
• RDF definitions are themselves Resources
  (linkage, see requirement 1)
• Properties
• slots, define relationships to other resources or
  atomic values
• Statements
• Resource has Property with Value
• (Values can be resources or atomic XML data)
• Similar to Frame Systems

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A Simple Example

• Statement
• Ora Lassila is the creator of the resource
  http//www.w3.org/Home/Lassila
• Structure
• Resource (subject) http//www.w3.org/Home/Las
  sila
• Property (predicate) http//www.schema.org/
  Creator
• Value (object) "Ora Lassila
• Directed graph

sCreator
http//www.w3.org/Home/Lassila
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Another Example

• To add properties to Creator, point through an
  intermediate Resource.

http//www.w3.org/Home/Lassila
sCreator
Person//fi/654645635
Email
Name
Ora Lassila
lassila_at_w3.org
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Example Bag

• The students incourse 6.001 are Amy, Tim,John,
  Mary,and Sue

RdfBag
rdftype
/Students/Amy
students
rdf_1
rdf_2
/Students/Tim
bagid1
rdf_3
/Students/John
rdf_4
/Students/Mary
rdf_5
/Students/Sue
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Example Alternative

• The source code for X11 may be found at
  ftp.x.org, ftp.cs.purdue.edu, or ftp.eu.net

http//x.org/package/X11
rdfAlt
rdftype
source
altid
rdf_1
ftp.x.org
rdf_2
ftp.cs.purdue.edu
rdf_3
ftp.eu.net
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Representing Prolog Facts in RDF
parent(pam,bob). parent(tom,bob). parent(tom,liz).
parent(bob,ann). parent(bob,pat). parent(pat,jim)
. female(pam). male(tom). male(bob). female(liz).
female(pat). female(ann). male(jim).
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OWLW3C Web Ontology Language

• OWL provides three increasingly expressive
  sublanguages OWL Lite, OWL DL, and OWL Full.

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OWLW3C Web Ontology Language
OWL Lite language constructs
RDF Schema Features Class rdfProperty
rdfssubClassOf rdfssubPropertyOf rdfsdomain
rdfsrange Individual
(In)Equality equivalentClass equivalentProperty
sameAs differentFrom allDifferent
Property Characteristics inverseOf
TransitiveProperty SymmetricProperty
FunctionalProperty InverseFunctionalProperty
Property Type Restrictions allValuesFrom
someValuesFrom
Restricted Cardinality minCardinality (only 0
or 1) maxCardinality (only 0 or 1) cardinality
(only 0 or 1)
Header Information ontology imports
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Ontology Spectrum
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Creating Your Own OntologyA Simple
Knowledge-Engineering Methodology

• Step 1 Determine the domain and scope of the
  ontology
• Why, what, who, competency questions
• Step 2 Consider reusing existing ontologies
• Step 3 Enumerate important terms in the ontology
• Step 4 Define the classes and the class
  hierarchy
• Step 5 Define the properties of classesslots
• Step 6 Define the facets of the slots
• Step 7 Create instances

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Obtaining RDF schema from ontology library

• SchemaWeb http//www.schemaweb.info/default.aspx
  
• Swoogle http//swoogle.umbc.edu/
• DAML ontology library http//www.daml.org/ontolog
  ies/

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Examples of RDF schema

• RSS 1.0 http//www.schemaweb.info/schema/SchemaDe
  tails.aspx?id12
• MusicBrainz http//www.schemaweb.info/schema/Sche
  maDetails.aspx?id168
• Resume http//www.schemaweb.info/schema/SchemaDet
  ails.aspx?id89
• FOAF http//www.schemaweb.info/schema/SchemaDetai
  ls.aspx?id29

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RDFCalendar
FOAF
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OWL-S Ontology for Semantic Web Services

• Some motivating tasks
• Automatic Web service discovery
• Automatic Web service invocation
• Automatic Web service composition and
  interoperation
• Automatic Web service execution monitoring

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High-level View of the Service Ontology
Service
Resource
provides
presents
supports
describedBy
ServiceProfile
ServiceGrounding
What the service does
How to Access it
ServiceModel
How it works
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Top Level of the Process Ontology
Input Precondition Output effect
hasProcess hasProfile
Process
Profile
Condition
Atomic Process
has Grounding
computedInput computedOutput computedEffect comput
edPrecondition invocab
Composite Process
expand collapse
realizes realizedBy
Simple Process
compsedBy
Control Construct
Sequence
Repeat Until
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Grounding a Service to a Concrete Realization
OWL-S
DL-Based Types
Process Model
Inputs/Outputs
Atomic Process
Message
Operation
Binding to SOAP, HTTP, etc.
WSDL
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SWRL Semantic Web Rule LanguageExamples
hasParent(?x1,?x2) ? hasBrother(?x2,?x3) ?
hasUncle(?x1,?x3) Implies(Antecedent(hasParent(I-
variable(x1) I-variable(x2))
hasBrother(I-variable(x2) I-variable(x3)))
Consequent(hasUncle(I-variable(x1)
I-variable(x3))))
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SPARQL RDF Query LanguageExamples
SELECT ?x WHERE ?x lthttp//www.w3.org/2001/vcar
d-rdf/3.0FNgt "John Smith" SELECT ?x, ?fname
WHERE ?x lthttp//www.w3.org/2001/vcard-rdf/3.0F
Ngt ?fname SELECT ?givenName WHERE ?y
lthttp//www.w3.org/2001/vcard-rdf/3.0Familygt
"Smith" . ?y http//www.w3.org/2001/vcard
-rdf/3.0Given ?givenName . PREFIX vcard
lthttp//www.w3.org/2001/vcard-rdf/3.0gt SELECT
?givenName WHERE ?y vcardFamily "Smith" . ?y
vcardGiven ?givenName . PREFIX vcard
lthttp//www.w3.org/2001/vcard-rdf/3.0gt SELECT
?g WHERE ?y vcardGiven ?g . FILTER
regex(?g, "r", "i") PREFIX info
lthttp//somewhere/peopleInfogt SELECT ?resource
WHERE ?resource infoage ?age .
FILTER (?age gt 24)
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Semantic Web System Architectures
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Typical System Architecture
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Layered Architecture
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System Architecture
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SesameA generic Architecture for Storing and
Querying RDF and RDF Schema
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Sesame
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Annotea Basic Architecture
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Knowledge Management Based on Semantic Web
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What is knowledge management?

• Knowledge is seen as a resource
• This means for knowledge management taking care
  that the resource is
• delivered at the right time
• available at the right place
• present in the right shape
• satisfying the quality requirements
• obtained at the lowest possible costs
• to be used in business processes

Selected from the course slides of CommonKADS
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Continuous improvement of knowledge assets
Knowledge assets
Construct new knowledge
Apply your best knowledge
Value chain
Selected from the course slides of CommonKADS
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Knowledge management knowledge engineering

• Organization analysis feeds into knowledge
  management (and vice versa)
• Knowledge modeling provides techniques for
  knowledge identification and development
• Knowledge engineering focuses on common /
  reusable elements in knowledge work

Selected from the course slides of CommonKADS
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Knowledge engineering

• process of
• eliciting,
• structuring,
• formalizing,
• operationalizing
• information and knowledge involved in a
  knowledge-intensive problem domain,
• in order to construct a program that can perform
  a difficult task adequately

Selected from the course slides of CommonKADS
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Problems in knowledge engineering

• complex information and knowledge is difficult to
  observe
• experts and other sources differ
• multiple representations
• textbooks
• graphical representations
• heuristics
• skills

Selected from the course slides of CommonKADS
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A Short History of Knowledge Systems
Selected from the course slides of CommonKADS
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CommonKADS Model Set
Selected from the course slides of CommonKADS
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Why context modeling?

• Often difficult to identify profitable use of
  (knowledge) technology
• Laboratory is different from the ''real'' world
• Acceptability to users very important
• Fielding into ongoing process not self evident
• Often not clear what additional measures to take

Selected from the course slides of CommonKADS
48
Goals for context modeling

• Identify problems and opportunities
• Decide about solutions and their feasibility
• Improve tasks and task-related knowledge
• Plan for needed organizational changes

Selected from the course slides of CommonKADS
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Role of knowledge systems

• "automation" is not the right way to look at KSs
• tasks are usually too complex
• much better view KS as process-improvement tool
• typical role of KS active intelligent assistant

Selected from the course slides of CommonKADS
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Context modelling process

• Step 1 Carry out a scoping and feasibility study
• Tool Organization Model (OM)
• Step 2 Carry out impact and improvement study
• Tool Task and Agent Models (TM, AM)
• zooming in/refinement of organization model
• Each study consists of an analysis part and a
  constructive decision-making part

Selected from the course slides of CommonKADS
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Project Management Based on Semantic Web
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Project Description
Organization model Task model Agent
model Knowledge model Communication model Design
models
Lesson Learned in PM
CommonKADS
Implementation using SW technology
System
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System Architecture
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(No Transcript)
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Communication Layer
Communication Services
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Communication Layer
Communication Services
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Communication Layer
Communication Services
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Team D
Team C
Team B
Communication Layer
Team A
??
??
??
Communication Services
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Communication Layer
Communication Services
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From Text to RDF
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Goal

• We aim at the automatic creation of metadata from
  documents for the Semantic Web using a low-cost
  natural language processing technology, i.e.,
  information extraction.

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System architecture for managing the metadata
layer of Semantic Web
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Integrating information extraction function with
Semantic Web system
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Components in the information extraction system
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IR vs. IE
IR
IE
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Extracted Domain Events in RDF
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Query the Extracted Contents
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A Test of the IE System

• We take one hundred financial news as our test
  data and use dozens of JAPE rules to extract the
  specific domain events.
• After the processing of domain events matching,
  we then calculate the precision rate and recall
  rate of our system.
• We first manually extract the target events
  within these financial news and we obtain 120
  events of interest.
• After the domain event matching, it returns 25
  results, among which 22 are correct. So the
  precision rate is 88, and the recall rate is
  18.

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Thank you.