Metadata for Web-based
Information Management
through Ontology
Dickson K. W. CHIU
Senior Member, IEEE & ACM
Dickson Computer Systems
Hong Kong
kwchiu@acm.org,
dicksonchiu@ieee.org
Poon, Joe Kit Man
Lam, Wai Chun
Tse, Chi Yung
Sui, William Hi Tai
Poon, Wing Sze
Department of Computer Science,
University of Hong Kong
Towards a Semantic Web

WWW is an impressive success:
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The current Web represents information using
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amount of available information (> 1 Giga-page)
number of human users (> 200 Mega-user)
natural language (English, Hungarian, Chinese,…)
graphics, multimedia, page layout
Humans can process this easily
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
Ontology
can deduce facts from partial information
can create mental associations
are used to various sensory information
 (well, sort of… people with disabilities may have serious
problems on the Web with rich media!)
Dickson Chiu - update 2011
Metadata - 2
Where are we now?
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Web 1.0: info-centric
Web 2.0: user-centric
Web 3.0: semantic-centric …
www.digitalrhetoric.org/course/web1to3.jpg
Ontology
Dickson Chiu - update 2011
Metadata - 3
Need for understanding Web info

Tasks often require to combine data on the Web:
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hotel and travel infos may come from different sites
searches in different digital libraries
Especially too much user provided content on Web 2.0
etc.
Again, humans combine these information easily
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even if different terminologies are used!
Ontology
Dickson Chiu - update 2011
Metadata - 4
What is the Problem?

Consider a typical web page:
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Ontology
Dickson Chiu - update 2011
Markup comprise
 rendering
information (e.g.,
font size and
colour)
 Hyper-links to
related content
Semantic content is
accessible to humans
but not (easily) to
computers…
Metadata - 5
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 …
Ontology
Dickson Chiu - update 2011
Metadata - 6
Information a machine may see…
WWW2002
The eleveth iteratioal world wide web coferece
Sherato waikiki hotel
Hoolulu, hawaii, USA
7-11 may 2002
1 locatio 5 days lear iteract
Registered participats comig from
australia, caada, chile demark, frace, germay,
ghaa, hog kog, idia, irelad, italy, japa,
malta, ew zealad, the etherlads, orway,
sigapore, switzerlad, the uited kigdom, the
uited states, vietam, zaire
Register ow
O the 7th May Hoolulu will provide the backdrop of
the eleveth iteratioal world wide web coferece.
This prestigious evet 
Speakers cofirmed
Tim berers-lee
Tim is the well kow ivetor of the Web, 
Ia Foster
Ia is the pioeer of the Grid, the ext geeratio
iteret 
Ontology
Dickson Chiu - update 2011
Metadata - 7
Solution: XML markup with “meaningful”
tags?
<name>WWW2002
The eleveth iteratioal world wide webco </name>
<location>Sherato
waikiki hotel
Hoolulu, hawaii, USA</location>…
How about…
<conf>WWW2002
The
eleveth iteratioal world wide webco</conf>
<place>Sherato
waikiki hotel
Hoolulu, hawaii, USA</place>
Then how about…
<会议>WWW2002
The
会议>
eleveth iteratioal world wide webco</
<地点>Sherato
waikiki hotel
地点>
Hoolulu, hawaii, USA</
Ontology
Dickson Chiu - update 2011
Metadata - 8
What Is Needed?
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A resource should provide information about itself
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Ontology
also called “metadata” (data about data)
Metadata capture part of the meaning of data
metadata should be in a machine processable format
agents should be able to “reason” about (meta)data
metadata vocabularies should be defined
Dickson Chiu - update 2011
Metadata - 9
What Is Needed (Technically)?
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To make metadata machine processable, we need:
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unambiguous names for resources (URIs)
a common data model for expressing metadata (RDF)
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and ways to access the metadata on the Web
common vocabularies (Ontologies)
The “Semantic Web” is a metadata based
infrastructure for reasoning on the Web
It extends the current Web (and does not replace it)
Ontology
Dickson Chiu - update 2011
Metadata - 10
Ontology: Origins and History

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Ontology in Philosophy - a philosophical discipline—a
branch of philosophy that deals with the nature and the
organization of reality
Science of Being (Aristotle, Metaphysics, IV, 1)
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Ontology
studies being or existence as well as the basic categories
thereof
trying to find out what entities and what types of entities exist
has strong implications for the conceptions of reality.
Dickson Chiu - update 2011
Metadata - 11
Ontology in Computer Science
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An ontology is an engineering artifact [Neches91]:
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defines basic terms and relations comprising the vocabulary of a topic
area
the rules for combining terms and relations to define extensions to the
vocabulary
“An explicit specification of a conceptualization” [Gruber93]
Formal specification of a shared conceptualization (of a certain domain)
[Borst 97]:
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Shared understanding of a domain of interest
Formal and machine manipulable model of a domain of interest
Ontology
Dickson Chiu - update 2011
Metadata - 12
History of the Semantic Web

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Web was “invented” by Tim Berners-Lee (amongst others), a physicist
working at CERN
TBL’s original vision of the Web was much more ambitious than the
reality of the existing (syntactic) Web:
“... a goal of the Web was that, if the interaction between person and
hypertext could be so intuitive that the machine-readable information
space gave an accurate representation of the state of people's thoughts,
interactions, and work patterns, then machine analysis could become
a very powerful management tool, seeing patterns in our work and
facilitating our working together through the typical problems which
beset the management of large organizations.”

TBL (and others) have since been working towards realising this vision,
which has become known as the Semantic Web

E.g., article in May 2001 issue of Scientific American…
Ontology
Dickson Chiu - update 2011
Metadata - 13
Adding “Semantics”
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External agreement on meaning of annotations
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E.g., Dublin Core (http://dublincore.org/)
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Problems with this approach
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Agree on the meaning of a set of annotation tags
Inflexible
Limited number of things can be expressed
Use Ontologies to specify meaning of annotations
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Ontologies provide a vocabulary of terms
New terms can be formed by combining existing ones
Meaning (semantics) of such terms is formally specified
Can also specify relationships between terms in multiple ontologies
Ontology
Dickson Chiu - update 2011
Metadata - 14
Some Technologies of Semantic Web
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RDF
XML
URI
SPARQL
XDI
XRI
SWRL
XFN
OWL
API
OAUTH
…
Dickson Chiu 2011
Semantic Web-15
Stamp Example – Google Search

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Now, suppose I Google for all red stamps
Not very intelligent…
Red stamps
Stamps from Cambodia (Khmer
Rouge)
Stamps from the Red Sea
Stamps from the 140th anniversary
of the Red Cross
Stamps with red dragons
Dickson Chiu 2011
Semantic Web-16
Stamp Example – Structural Meaning
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Not very intelligent, but how can a computer know
what I mean?
When we structurally describe that
a stamp is a stamp and red is a color.
Describing data in a structured way can best be done
in a database.
Different databases can be connected.
Dickson Chiu 2011
Semantic Web-17
Stamp Example – All about a Stamp
In 1980 you could buy this stamp for 1 cent
This is a stamp
Now it’s worth 3 euros
This stamp is from the United Kingdom
This stamp is used between 1978 - 1981
The picture on the stamp is a PO Box
This stamp is designed by John Bryan Dunmore
Dickson Chiu 2011
Semantic Web-18
XML
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Meaning is about understanding.
To understand we need a language.
A language starts with words.
Things mean something in words.
Online, we describe things with XML.
Dickson Chiu 2011
Semantic Web-19
XML - Example
<?xml version="1.0" encoding="ISO-8859-1"?>
<collection name=”My stamp collection">
<stamp>
<title>Red dragon</title>
<country>China</country>
<year>1984</year>
</stamp>
<stamp>
<title>PO Box</title>
<country>England</country>
<year>1992</year>
</stamp>
</collection>
Dickson Chiu 2011
Semantic Web-20
RDF and RDF Schema
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Resource Description Framework (RDF)
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RDF Schema is a vocabulary description language
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We can’t understand words alone
RDF is a data model for objects and relations between them
In addition, online grammar is required
Describes classes and properties of RDF resources
Provides semantics for generalization hierarchies of properties
and classes
With RDF Schema we can define concepts and make
simple relations between them.
Dickson Chiu 2011
Semantic Web-21
RDF Example
This stamp is from England
Predicate
object
subject
hence from Europe.
Dickson Chiu 2011
Semantic Web-22
RDF Schema Example
Stamp
Country
from
in
Continent
Dickson Chiu 2011
Semantic Web-23
OWL
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But, RDF schema is limited.
A language needs more expression and logic to make
good reasoning possible.
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relations between classes
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cardinality
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
e.g., disjointness
e.g. “exactly one”
richer typing of properties
That’s why OWL (The Web Ontology Language) was
invented.
characteristics of properties (e.g., symmetry)
BOTH OWL and RDF are standards of
www.w3.org
Ontology
Dickson Chiu - update 2011
Metadata - 24
SWRL
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Finally, to reason, you need rules.
Rules are formulated in SWRL (Semantic Web Rule
Language)
Dickson Chiu 2011
Semantic Web-25
SWRL Example
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I got this stamp from
my uncle.
The rule for calling
someone my uncle is
that one of my parents
has a brother.
<ruleml:imp>
<ruleml:_rlab ruleml:href="#example1"/>
<ruleml:_body>
<swrlx:individualPropertyAtom swrlx:property="hasParent">
<ruleml:var>x1</ruleml:var>
<ruleml:var>x2</ruleml:var>
</swrlx:individualPropertyAtom>
<swrlx:individualPropertyAtom swrlx:property="hasBrother">
<ruleml:var>x2</ruleml:var>
<ruleml:var>x3</ruleml:var>
</swrlx:individualPropertyAtom>
</ruleml:_body>
<ruleml:_head>
<swrlx:individualPropertyAtom swrlx:property="hasUncle">
<ruleml:var>x1</ruleml:var>
<ruleml:var>x3</ruleml:var>
</swrlx:individualPropertyAtom>
</ruleml:_head>
</ruleml:imp>
brother
son of
I
mother or father
Dickson Chiu 2011
Semantic Web-26
SPARQL
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Suppose, I want to search for a specific stamp.
“I want all the red stamps, designed in Europe, but
used in the U.S.A., between 1980 and 1990”
We can use SPARQL (Protocol and RDF Query
Language).
Dickson Chiu 2011
Semantic Web-27
URI
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Because the web is decentralized and data is in many
places, not only language is important.
Exchange of data between different machines is key.
To make a connection a machine needs a source. For this,
we use resource identifiers.
Best known resource identifier is the URI
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which consists of a name (urn) and a location (url)
URI
URN
Red PO Box
URL
http://www.mystampcollection.com/redpobox
Dickson Chiu 2011
Semantic Web-28
XRI & XDI
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URIs have international limitations and the need for
data-exchange between machines is rapidly growing.
There is a successor: XRI (Extensible Resource
Identifier)
There is a standard for sharing, linking and
synchronizing data.
This standard is called XDI (XRI Data Interchange).
Dickson Chiu 2011
Semantic Web-29
OAuth API
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However, data is often protected.
We need consent and a key to gain access.
The key to certain data is described in an API (an
application programming interface).
An open standard for accessing (authentication) the
API is OAuth.
Dickson Chiu 2011
Semantic Web-30
Berner-Lee’s Architecture
???
???
SWRL
???
 Semantics+reasoning
 Relational Data
 Data Exchange
?
OWL
?
• Relationship between layers is not clear
• OWL extends of RDF / schema
Ontology
Dickson Chiu - update 2011
Metadata - 31
Ontology Elements
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Concepts (classes) + their hierarchy
Concept properties (slots / attributes)
Property restrictions (type, cardinality, domain, etc.)
Relations between concepts (disjoint, equality, etc.)
Instances
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E-R diagram / UML diagram ???
Note: “Property”  “Slot”  “Relation”  “Relationtype” 
“Attribute”  Semantic link type”
Ontology
Dickson Chiu - update 2011
Metadata - 32
The Role of Ontologies on the Web
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Ontologies provide a shared understanding of a domain: semantic
interoperability
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Ontologies are useful for the organization and navigation of Web sites
Ontologies are useful for improving the accuracy of Web searches
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search engines can look for pages that refer to a precise concept in an
ontology
Web searches can exploit generalization/ specialization information
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overcome differences in terminology
mappings between ontologies
If a query fails to find any relevant documents, the search engine may
suggest to the user a more general query.
If too many answers are retrieved, the search engine may suggest to the
user some specializations.
General e-business automation based on understanding web resource
in order to facilitate intelligent (software agent) processing
Ontology
Dickson Chiu - update 2011
Metadata - 33
Case study: Use of Ontology
in an e-Marketplace
D.K.W. Chiu, J.K.M. Poon, W.C. Lam, C.Y. Tse, W.H.T.
Siu, W.S. Poon. How Ontologies Can Help in an Emarketplace, European Conference on Information
Systems 2005 (ECIS 2005), May 2005
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Semantic Web vision is probably too ambitious
A more realistic current application that has a potential
to become a killer application
Ontology
Dickson Chiu - update 2011
Metadata - 34
Motivation
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Compare some general-purposed
e-Marketplaces (auction based)
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e-Bay (HK): www.ebay.com.hk
Yahoo Auction (HK): auctions.yahoo.com.hk
Taobao owned by Alibaba.com: http://www.taobao.com
(See also Alibaba.com: http://china.alibaba.com/)
Compare special-purposed e-Marketplaces
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Airtickets: http://www.qunar.com/
Finding friends (!): http://www.meetu.hk/
Which one is better? Why?
Key issue => capturing and applying domain knowledge
Ontology
Dickson Chiu - update 2011
Metadata - 35
What is an e-Marketplace?
Suppliers
offers
bids
offers
Buyers
Ontology
e-Marketplace
Aggregate requests
from Buyers, contact
potential Suppliers,
match Suppliers
and Buyers, exchange
bids and offers,
generate e-Contract
Repository
Ontologies and Concepts
e-Negotiation data
Agreements
…
bids
Dickson Chiu - update 2011
Metadata - 36
Problem Statements
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Are there currently significant practical use of the
Ontology from Semantic Web?
Match-making and beyond
Software requirement engineering / negotiation
Model and solve practical problems with CS & ICT
Cross-over multi-disciplinary research
IJSSOE: Dickson Chiu, Editor-in-chief
http://www.igi-global.com/journals/details.asp?id=34268
Ontology
Dickson Chiu - update 2011
Metadata - 37
Example Ontology
Clothing and Sales Negotiation
{unordered} attributes:
deposit, installment,
pay-upon-delivery, ...
Payment Terms
Refunding Policy
Discount
Delivery
Total Amount
Sale Order
Clothing *
{ordered} attributes:
small, medium, large,
extra-large
Appearance
Size
{unordered}
attributes: brick
red, crimson, ...
Ontology
Shipping
Cost
Quantity
Unit Cost
Delivery Date
Insured Amount
Color
Red
Payee
Purple
Insurer
Insurance
Premium
{unordered}
attributes: light
purple, magenta, ...
Dickson Chiu - update 2011
Metadata - 38
Objective and Solution Approach
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How to elicit negotiation requirements?
Semantic Web
=> Ontologies
=> help negotiators’ mutual understanding of
issues, alternatives, and tradeoffs
Address semantic requirements of negotiation
Reduce cost and improve effectiveness of negotiation
(avoid combinatorial explosion of issues)
Development of an effective and efficient negotiation
plan
Applications: e-Marketplace, Web-service negotiation,
agent negotiation, requirement negotiation…
Ontology
Dickson Chiu - update 2011
Metadata - 39
Semantic based
e-Marketplace Conceptual Model
2..n
Negotiation
Trader
Ontology
n
Matchmaking
Auxiliary Concept
Base Concept
Recommendation
1..n
1..n
1..n
1..n
Task
1
1
1
evaluates
1..n
Offer
Accepted Offer
Ontology
n
resolves
drives
1..n
Issue
1
maps to
formulates
1
Decision Plan
1
precedes
Concept n
n
1..n
n
1..n
n
indivisibly relates to
1..n
1..n
Alternative Value
Accepted Alternative Value
Dickson Chiu - update 2011
Metadata - 40
Overall e-Negotiation Process
Design Methodology
Requirements
elicitation
phase
Trader select agreed
relevant ontologies
for each
collection of
co-related
issue
System maps issues
into ontology concepts
[not consistent]
[consistent]
System derive
concept relations
System check consistency
of issues & concepts
Trader identify
issues
System identifies
alternatives
Requirements
elicitation
phase
System formulate
decision plan
[need to identify
new issues]
[need to revise
tradeoff model]
[need to identify
new issues]
System creation of
agreement
[all issues are
resolved]
System supported
trader negotiation
[negotiation
target chosen]
[quit
negotiation] [accept offer]
Trader post (revised)
Trader product
preferences as offer
selection
[reject all matches/
recommendations]
Ontology
System performs
recommendation
[match not found]
System performs
matchmaking
Trader specifies alternative
values of issues
Decision
phase
[match found]
[trader change requirements]
Decision
phase
Dickson Chiu - update 2011
Metadata - 41
Requirement Elicitation Methodology
1.
2.
3.
4.
5.
6.
Traders select agreed ontology.
Traders relate requirements to concepts in the selected ontology.
System checks dependencies of concepts that constitute all the
requirements from the (refined) ontology map. Mutually dependent
clusters of concepts determine the indivisible groups of requirements
that have to be considered together so that effective tradeoff can be
evaluated.
The system checks the consistency of all the concepts, issues, and their
dependencies (Cheung et al. 2002).
For a consistent plan, the system can proceed to elicit the possible
alternatives; otherwise we have to re-iterate from step 3.
According to the dependencies, the system can formulate a precedence
graph of the requirements and requirements groups. Based on the
precedence graph, an efficient decision plan can be determined.
Ontology
Dickson Chiu - update 2011
Metadata - 42
Decision Phase Methodology
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The system
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Trader may accept any matched offers
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or change his reservation price and attempt a negotiation with those offers in
order to seek for a more favorable one.
If no matching offers are found, the system identifies near misses and
also attempts to rank them for the trader to choose.
Trader change his mind to accept a near miss
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searches for the matching offers based on the trader’s preference
attempt to rank them for the trader to choose
or choose a near miss for negotiation.
During negotiation, the system supports the user to make and evaluate
offers / counter-offers based on the decision plan (from previous slide) in
a negotiation session as follows (Chiu et al. 2005).
Should new requirement issues arise in the decision phase (say, due to
incomplete specification), the trader can we can go back to analyze the
new issue and its relationships to the existing ones.
In real-life, the formulation of a decision plan may involve several
iterations. This reflects the traders may not be able to understand all the
inter-relationships among the issues in one shot.
Ontology
Dickson Chiu - update 2011
Metadata - 43
Understanding Requirements from
Ontologies
Perform graph search algorithm on the semantic map
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Key requirements are preliminary identified in the first
round (e.g., unit price, quantity)
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For each identified requirement issue,
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check if an issue can be mapped directly to a concept.
If not, see if an issue can be refined into a set of more specific
concepts
a cost is refined into constituent costs that sum up to it.
Incomplete Ontologies
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Ontology
Introduce new concepts into the ontology map
Relate it with to existing ones
Dickson Chiu - update 2011
Metadata - 44
Understanding Requirements from
Ontology (Cont)
Perform graph search algorithm on the semantic map
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For each identified concept c,
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Examine every un-visited node n adjacent to c in the ontology
map.
For each such node n, see if the new concept is relevant to the
negotiation problem.
Repeat until no more related new concepts can be identified.
Only after successful deal do we need to consider
combining newly identified working concepts back to
more concise real-life objects in specifying a agreement

Ontology
E.g., component costs need not shown to business partner
Dickson Chiu - update 2011
Metadata - 45
Understanding Dependencies of
Requirements from Ontologies
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Functional dependency
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borrowed from fundamental relational database concepts
motivate this research
The alternative for an issue is determined by the
alternatives(s) of other issue(s).
E.g., delivery date and quantity -> cost of production
Computational dependency
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Ontology
more obvious type of functional dependency
hardwired computational formula
E.g., insurance amount = percentage * cost of goods.
Dickson Chiu - update 2011
Metadata - 46
Understanding Dependencies of
Requirement from Ontology
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Requirement dependency (constraint satisfaction)
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Only after the determinant value is known can viable
alternatives be determined.
E.g., whether a customer may pay by credit card, bank draft,
or remittance is evaluated according to the total amount.
Classification dependency
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Ontology
A special type of requirement dependency in which the
classification of another issue is dependent on the outcome
of an agreed issue.
E.g., customer tiering
Dickson Chiu - update 2011
Metadata - 47
Indivisible Requirement Components for
Tradeoff Evaluation
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Indivisible Components of Issues
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Cyclic dependencies among the concepts
Tradeoff Evaluation
Topological sort of semantic graph gives negotiation plan
Determine
Payment Terms
Determine Size
Determine Shipping
Cost and Payee
Determine Unit Cost,
Quantity & Delivery Date
Determine Insurance Premium,
Insured Amount & Insurer
Determine Color
Determine
Refund Policy
Compute Total
Amount
Ontology
Determine
Discount
Dickson Chiu - update 2011
Metadata - 48
Understanding Possible Requirement
Alternatives from Ontology
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Alternative for requirements are often in discrete values
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cannot be expressed in numerical values
not quantized in normal practices because of difficulties in
recognizing them, e.g., color
for simplicity and convenience (size => S, M, L, XL)
The elicitation of options is streamlined when a
complicated issue is decomposed into concepts
(appearance => size + color + shapes)
Ontology provide
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explicit ordering of them (size => S < M < L < XL)
implicit ordering
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Ontology
inheritance (“is-a”) hierarchies
composition hierarchies
Dickson Chiu - update 2011
Metadata - 49
Exploring more trading opportunities
from Ontology
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Improve the accessibility of automated agents to
match functional specification
Intelligent software agents could represent buyers or
sellers
e-marketplace acts as “broker”
Consider shared ontology attributes and constraints
Map for cross-sale
Group buyers or sellers together for higher market
efficiencies
Better hints for data mining
Ontology
Dickson Chiu - update 2011
Metadata - 50
System Implementation Architecture
Multiplatform Support Subsystem
WAP
Gateway
Multiplatform
Devices
Internet
Messenger
SMS
Gateway
e-Negotiating Matching Subsystem
Web Server
e-Negotiation
Process Generator
task
dependency
Task
Organizer
e-Negotiation process
bids & offers
e-Negotiation Executing Subsystem
issue
dependency
ontology
Issue
Ontology Maintenance Subsystem
e-Negotiation
Session Manager
Ontology
Generator
ontology
ontology
e-Negotiation process
Ontology
Issue Dependency
Editor
revised ontology,
issues
e-Negotiation
Data &
Repository
Search
Engine
Ontology
Editor
Criteria
Issue
Criteria &
Issues Editor
existing
ontology
Dickson Chiu - update 2011
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OWL Listing
<rdf:rest rdf:resource="http://www.w3.org/1999/02/22-rdf-syntaxns#nil"/>
<owl:Ontology rdf:about="#Clothing">
<rdf:first
<rdfs:comment>Sample Clothing
rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Small
Ontology</rdfs:comment>
</rdf:first></rdf:List></rdf:rest>
<owl:Class rdf:ID="Clothing" />
<rdf:first
<owl:Class rdf:ID="Appearance" />
rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Medi
<owl:Class rdf:ID="Color">
um</rdf:first></rdf:List></rdf:rest>
<rdfs:subClassOf rdf:resource="#Appearance" /> <rdf:first
rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Large
...
</rdf:first></rdf:List></rdf:rest>
</owl:Class>
<rdf:first
<owl:ObjectProperty rdf:ID="hasAppearance">
rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Extra
<rdfs:domain rdf:resource="#Clothing" />
Large</rdf:first></rdf:List>
<rdfs:range rdf:resource="#Appearance" />
</owl:oneOf></owl:DataRange></rdfs:range>
</owl:ObjectProperty>
</owl:DatatypeProperty>
<owl:ObjectProperty rdf:ID="hasColor">
<owl:Class rdf:ID=" UnitCost"> …
<rdfs:subPropertyOf
<owl:equivalentClass> <!-- unit cost depends on appearance -->
rdf:resource="hasClothAppearance" />
<owl:Restriction> <owl:someValuesFrom
<rdfs:range rdf:resource="#Color” />
rdf:resource="#Appearance" /> </owl:Restriction>
...
</owl:equivalentClass>
</owl:ObjectProperty>
</owl:Class>…
<owl:DatatypeProperty rdf:ID="size"> <!-- Enumeration </owl:Ontology>
--!>
<rdfs:domain rdf:resource="#Appearance"/>
<rdfs:range> <owl:DataRange> <owl:oneOf> <rdf:List>
<rdf:rest> <rdf:List> <rdf:rest><rdf:List>
<rdf:rest><rdf:List>
Ontology
Dickson Chiu - update 2011
Metadata - 52
Summary
Function
Traditional e-marketplace problem
Contributions of Ontology
Matchmaking
Match-making is often ineffective because of the
rigid definition of products of limited attributes.
Shared and agreed ontology provides common, flexible, and
extensible definitions of products and requirements for matchmaking and subsequent business processes
It is difficult to specify complex product
requirements because the relationships among
attributes and values are ignored.
Complicated requirements can be decomposed into simple
concepts for streamlining the elicitation of options
User interactions are limited to mainly manually,
which is time consuming.
Accessible by automated agents through Semantic Web
specifications for more business opportunities
Recommendations are often only possible within
the same category.
Ontology helps elicit alternatives for recommendation.
Pre-set formulae for every type of product are
needed for evaluation.
Ontology help recommendation by evaluating offers in terms
of flexible overall scaling
Cross-sale and grouping of buyers and sellers
with similar requests are difficult.
Matching grouping of buyers and sellers as well as cross-sale
possible by inference with the ontology.
No implicit ordering of alternatives.
Implicit ordering of alternatives is elicited via inheritance.
Manual negotiation or inadequate negotiation
support cause inefficient process and ineffective
recognition.
Machine understandable semantics facilitate negotiation and
automatic configuration of products and services as specified.
Recommendation
Negotiation
Ontology
Dickson Chiu - update 2011
Metadata - 53
Conclusions
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Formulation of negotiation plan with maturing of Semantic
Web technologies
Elicitation of negotiation issues, issue dependencies,
tradeoff, and alternatives
Control the openness of issues
Our algorithm verifies the completeness of elicited
negotiation requirements
Negotiation processes are properly guided, recorded, and
managed
For e-commerce activities are usually more structural and
repeatable (as opposed to political negotiations)
Ontologies and plans are therefore reusable
Negotiation automation with agents / integration with EIS
Ontology
Dickson Chiu - update 2011
Metadata - 54
Future Work
 Formal models
 Elicitation of semantic distances
 enhancement of ontology-based matchmaking and
recommendation algorithms
 ontology-based cross-sale and up-sale
 grouping of buyers and sellers for combined quantity deals
 mobile clients and constraint-based requirement
specification
Ontology
Dickson Chiu - update 2011
Metadata - 55
Summary
Dickson Chiu 2011
Limitations of Current IM
Technologies
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Searching information
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Extracting information
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human involvement necessary for browsing, retrieving,
interpreting, combining
Maintaining information
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Keyword-based search engines
inconsistencies in terminology, outdated information.
Viewing information
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Impossible to define views on Web knowledge
Dickson Chiu 2011
Semantic Web-57
Ontology based IM
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Information / knowledge will be organized in
conceptual spaces according to its meaning.
Automated tools for information maintenance and
knowledge discovery
Semantic query answering
Query answering over many documents
Defining who may view certain parts of information
(even parts of documents) will be possible.
Dickson Chiu 2011
Semantic Web-58
Agent-base IM
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An agent is a computer system that is capable of flexible,
autonomous action on behalf of its user or owner in order to
meet its design objectives in a designated environment.
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Many other definitions …
Your own personal (digital) automatic assistant
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knows about your preferences
builds up knowledge base using your past
can combine the local knowledge with remote services:
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hotel reservations, airline preferences
dietary requirements
medical conditions
calendaring
etc
It communicates with remote information (i.e., on the Web!)
All the above can be facilitated with ontology
Dickson Chiu 2011
Semantic Web-59
Intelligent Agents & Ontology
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Metadata
 Identify and extract
information from Web
sources
Ontologies
 Web searches, interpret
retrieved information
 Communicate with other
agents
Logic
 Process retrieved
information, draw
conclusions
Dickson Chiu 2011
Semantic Web-60
Agent: B2C Electronic Commmerce
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A typical scenario: user visits one or several online
shops, browses their offers, selects and orders
products.
Ideally humans would visit all, or all major online
stores; but too time consuming
Current shopbots required too much programming
Software agents that can interpret the product
information and the terms of service.
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Pricing and product information, delivery and privacy policies
will be interpreted and compared to the user requirements.
Information about the reputation of shops
Sophisticated shopping agents will be able to conduct
automated negotiations
Dickson Chiu 2011
Semantic Web-61
Example: Database Integration
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Databases are very different in structure, in content
Lots of applications require managing several databases
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after company mergers
combination of administrative data for e-Government
biochemical, genetic, pharmaceutical research
etc.
Most of these data are now on the Web
The semantics of the data(bases) should be known
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how this semantics is mapped on internal structures is immaterial
Dickson Chiu 2011
Semantic Web-62
Example: Digital Libraries
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It is a bit like the search example
It means catalogs on the Web
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librarians have known how to do that for centuries
goal is to have this on the Web, World-wide
extend it to multimedia data, too
Ontology encodes metadata
But it is more: software agents should also be
librarians!
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help you in finding the right publications
Dickson Chiu 2011
Semantic Web-63
Content Management via Metadata
album pages
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artist bios
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album reviews
How to build an inventory for
collection and search of
content objects?
How to deal with multiple
content object types?
What contextual navigation
should exist between these
content objects?
How can we use metadata
technique as the solution?
64
Example Album Ontology
concert calendar
album pages
artist bios
TV listings
album reviews
Ontology
Dickson Chiu - update 2011
Metadata - 65
Video Content Ontology Example
Subscription history
User Profile
Discount
Market offer
Subscription
Year
Content
Cast
Genre
{ordered}
attributes: I,
IIA, IIB, III
Class
Category
{unordered}
attributes: Fun,
Documentary,
Drama, …
BSIM0012
Director
Language
{unordered}
attributes: Chin,
Eng, France,
German, …
66
{unordered}
attributes: Andy
Lau, Faye
Wong, …
{unordered}
attributes: Ang
Lee, Dante Lam,
Andy Fickman,…
Question and Answer
Thank you!
Email: dicksonchiu@ieee.org
Ontology
Dickson Chiu - update 2011
Metadata - 67