1. Metadata for Web-based Information Management through Ontology
Dickson K. W. CHIU
Senior Member, IEEE & ACM
Dickson Computer Systems
Hong Kong
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

2. Towards a Semantic Web WWW is an impressive success:
amount of available information (> 1 Giga-page)
number of human users (> 200 Mega-user)
The current Web represents information using
natural language (English, Hungarian, Chinese,…)
graphics, multimedia, page layout
Humans can process this easily
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!)
Ontology
Dickson Chiu - update 2011

3. Where are we now? Web 1.0: info-centric Web 2.0: user-centric
Web 3.0: semantic-centric …
Ontology
Dickson Chiu - update 2011

4. Need for understanding Web info
Tasks often require to combine data on the Web:
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
even if different terminologies are used!
Ontology
Dickson Chiu - update 2011

5. What is the Problem? Consider a typical web page: 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…
Consider a typical web page:
Ontology
Dickson Chiu - update 2011

6. 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

7. Information a machine may 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

8. Solution: XML markup with “meaningful” tags?
<name>WWW2002
The eleventh international world wide webcon</name>
<location>Sheraton waikiki hotel
Honolulu, hawaii, USA</location>…
How about…
<conf>WWW2002
The eleventh international world wide webcon</conf>
<place>Sheraton waikiki hotel
Honolulu, hawaii, USA</place>
Then how about…
<会议>WWW2002
The eleventh international world wide webcon</会议>
<地点>Sheraton waikiki hotel
Honolulu, hawaii, USA</地点>
Ontology
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9. What Is Needed? A resource should provide information about itself
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
Ontology
Dickson Chiu - update 2011

10. What Is Needed (Technically)?
To make metadata machine processable, we need:
unambiguous names for resources (URIs)
a common data model for expressing metadata (RDF)
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

11. Ontology: Origins and History
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)
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.
Ontology
Dickson Chiu - update 2011

12. Ontology in Computer Science
An ontology is an engineering artifact [Neches91]:
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]:
Shared understanding of a domain of interest
Formal and machine manipulable model of a domain of interest
Ontology
Dickson Chiu - update 2011

13. History of the Semantic Web
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:
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…
“... 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.”
Ontology
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14. Adding “Semantics” External agreement on meaning of annotations
E.g., Dublin Core (
Agree on the meaning of a set of annotation tags
Problems with this approach
Inflexible
Limited number of things can be expressed
Use Ontologies to specify meaning of annotations
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

15. Some Technologies of Semantic Web
RDF
XML
URI
SPARQL
XDI
XRI
SWRL
XFN
OWL
API
OAUTH …
Dickson Chiu 2011

16. Stamp Example – Google Search
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

17. Stamp Example – Structural Meaning
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

18. Stamp Example – All about a Stamp
In 1980 you could buy this stamp for 1 cent
Now it’s worth 3 euros
This is a stamp
This stamp is from the United Kingdom
This stamp is used between
The picture on the stamp is a PO Box
This stamp is designed by John Bryan Dunmore
Dickson Chiu 2011

19. XML 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

20. XML - Example
<?xml version="1.0" encoding="ISO "?> <collection name=”My stamp collection"> <stamp> <title>Red dragon</title> <country>China</country> <year>1984</year> </stamp> <title>PO Box</title> <country>England</country> <year>1992</year> </collection>
Dickson Chiu 2011

21. RDF and RDF Schema Resource Description Framework (RDF)
We can’t understand words alone
RDF is a data model for objects and relations between them
RDF Schema is a vocabulary description language
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

22. RDF Example This stamp is from England Predicate object subject
hence from Europe.
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23. RDF Schema Example
Country
Stamp
from in
Continent
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24. OWL But, RDF schema is limited.
A language needs more expression and logic to make good reasoning possible.
relations between classes
e.g., disjointness
cardinality
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
Ontology
Dickson Chiu - update 2011

25. SWRL Finally, to reason, you need rules.
Rules are formulated in SWRL (Semantic Web Rule Language)
Dickson Chiu 2011

26. SWRL Example I mother or father I got this stamp from my uncle.
<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>x3</ruleml:var>
</ruleml:_body>
<ruleml:_head>
<swrlx:individualPropertyAtom swrlx:property="hasUncle">
</ruleml:_head>
</ruleml:imp>
I got this stamp from my uncle.
The rule for calling someone my uncle is that one of my parents has a brother.
son of
brother I
mother or father
Dickson Chiu 2011

27. SPARQL 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

28. URI
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
which consists of a name (urn) and a location (url)
URI
URN
Red PO Box
URL
Dickson Chiu 2011

29. XRI & XDI
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

30. OAuth API 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

31. Berner-Lee’s Architecture
???
SWRL
OWL
 Semantics+reasoning ?
 Relational Data ?
 Data Exchange
Relationship between layers is not clear
OWL extends of RDF / schema
Ontology
Dickson Chiu - update 2011

32. Ontology Elements Concepts (classes) + their hierarchy
Concept properties (slots / attributes)
Property restrictions (type, cardinality, domain, etc.)
Relations between concepts (disjoint, equality, etc.)
Instances
E-R diagram / UML diagram ???
Note: “Property”  “Slot”  “Relation”  “Relationtype”  “Attribute”  Semantic link type”
Ontology
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33. The Role of Ontologies on the Web
Ontologies provide a shared understanding of a domain: semantic interoperability
overcome differences in terminology
mappings between ontologies
Ontologies are useful for the organization and navigation of Web sites
Ontologies are useful for improving the accuracy of Web searches
search engines can look for pages that refer to a precise concept in an ontology
Web searches can exploit generalization/ specialization information
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
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34. 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 E-marketplace, European Conference on Information Systems 2005 (ECIS 2005), May 2005
Semantic Web vision is probably too ambitious
A more realistic current application that has a potential to become a killer application
Ontology
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35. Motivation
Compare some general-purposed e-Marketplaces (auction based)
e-Bay (HK):
Yahoo Auction (HK): auctions.yahoo.com.hk
Taobao owned by Alibaba.com:
(See also Alibaba.com:
Compare special-purposed e-Marketplaces
Airtickets:
Finding friends (!):
Which one is better? Why?
Key issue => capturing and applying domain knowledge
Ontology
Dickson Chiu - update 2011

36. What is an e-Marketplace?
Suppliers e -
Marketplace
offers
Aggregate requests
Repository
bids
from Buyers, contact
potential Suppliers,
Ontologies and Concepts
match Suppliers e -
Negotiation data
offers
and Buyers, exchange
Agreements - …
bids and offers,
generate e -
Contract
bids
Buyers
Ontology
Dickson Chiu - update 2011

37. Problem Statements
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
Ontology
Dickson Chiu - update 2011

38. Example Ontology Clothing and Sales Negotiation
Quantity
Purple
Red
Discount
Total Amount
Refunding Policy
Color
Size
Appearance
Clothing
Unit Cost
Payee
Insured Amount
Insurer
Premium
{unordered} attributes:
deposit, installment,
pay-upon-delivery, ...
{unordered}
attributes: brick
red, crimson, ...
{ordered} attributes:
small, medium, large,
extra-large
attributes: light
purple, magenta, ...
Delivery Date
Sale Order *
Delivery
Shipping
Cost
Payment Terms
Insurance
Ontology
Dickson Chiu - update 2011

39. Objective and Solution Approach
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

40. Semantic based e-Marketplace Conceptual Model
Ontology
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41. Overall e-Negotiation Process Design Methodology
Requirements
elicitation
phase
Decision
phase
Ontology
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42. Requirement Elicitation Methodology
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

43. Decision Phase Methodology
The system
searches for the matching offers based on the trader’s preference
attempt to rank them for the trader to choose
Trader may accept any matched offers
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
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

44. Understanding Requirements from Ontologies
Perform graph search algorithm on the semantic map
Key requirements are preliminary identified in the first round (e.g., unit price, quantity)
For each identified requirement issue,
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
Introduce new concepts into the ontology map
Relate it with to existing ones
Ontology
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45. Understanding Requirements from Ontology (Cont)
Perform graph search algorithm on the semantic map
For each identified concept c,
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
E.g., component costs need not shown to business partner
Ontology
Dickson Chiu - update 2011

46. Understanding Dependencies of Requirements from Ontologies
Functional dependency
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
more obvious type of functional dependency
hardwired computational formula
E.g., insurance amount = percentage * cost of goods.
Ontology
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47. Understanding Dependencies of Requirement from Ontology
Requirement dependency (constraint satisfaction)
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
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
Ontology
Dickson Chiu - update 2011

48. Indivisible Requirement Components for Tradeoff Evaluation
Indivisible Components of Issues
Cyclic dependencies among the concepts
Tradeoff Evaluation
Topological sort of semantic graph gives negotiation plan
Ontology
Dickson Chiu - update 2011

49. Understanding Possible Requirement Alternatives from Ontology
Alternative for requirements are often in discrete values
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
explicit ordering of them (size => S < M < L < XL)
implicit ordering
inheritance (“is-a”) hierarchies
composition hierarchies
Ontology
Dickson Chiu - update 2011

50. Exploring more trading opportunities from Ontology
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

51. System Implementation Architecture
Multiplatform Support Subsystem
WAP
Gateway
SMS
Internet
Messenger
Web Server
e-Negotiation Executing Subsystem
e-Negotiation
Session Manager
Ontology
Generator
e-Negotiating Matching Subsystem
Process Generator
Task
Organizer
Issue Dependency
Editor
issue
dependency
task
Ontology Maintenance Subsystem
Search
Engine
Criteria &
Issues Editor
ontology
Criteria
Issue
bids & offers
e-Negotiation process
revised ontology,
issues
existing
Data &
Repository
Multiplatform
Devices
Ontology
Dickson Chiu - update 2011

52. OWL Listing
<rdf:rest rdf:resource="
<rdf:first rdf:datatype="
<rdf:first rdf:datatype="
<rdf:first rdf:datatype="
<rdf:first rdf:datatype=" Large</rdf:first></rdf:List>
</owl:oneOf></owl:DataRange></rdfs:range>
</owl:DatatypeProperty>
<owl:Class rdf:ID=" UnitCost"> …
<owl:equivalentClass> <!-- unit cost depends on appearance -->
<owl:Restriction> <owl:someValuesFrom rdf:resource="#Appearance" /> </owl:Restriction>
</owl:equivalentClass>
</owl:Class>…
</owl:Ontology>
<owl:Ontology rdf:about="#Clothing">
<rdfs:comment>Sample Clothing Ontology</rdfs:comment>
<owl:Class rdf:ID="Clothing" />
<owl:Class rdf:ID="Appearance" />
<owl:Class rdf:ID="Color">
<rdfs:subClassOf rdf:resource="#Appearance" />
...
</owl:Class>
<owl:ObjectProperty rdf:ID="hasAppearance">
<rdfs:domain rdf:resource="#Clothing" />
<rdfs:range rdf:resource="#Appearance" />
</owl:ObjectProperty>
<owl:ObjectProperty rdf:ID="hasColor">
<rdfs:subPropertyOf rdf:resource="hasClothAppearance" />
<rdfs:range rdf:resource="#Color” />
<owl:DatatypeProperty rdf:ID="size"> <!-- Enumeration --!>
<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

53. Summary Function Traditional e-marketplace problem
Contributions of Ontology
Match-making
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 match-making 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
Recom-mendation
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.
Negotiation
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.
Ontology
Dickson Chiu - update 2011

54. Conclusions
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

55. 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

56. Summary
Dickson Chiu 2011

57. Limitations of Current IM Technologies
Searching information
Keyword-based search engines
Extracting information
human involvement necessary for browsing, retrieving, interpreting, combining
Maintaining information
inconsistencies in terminology, outdated information.
Viewing information
Impossible to define views on Web knowledge
Dickson Chiu 2011

58. Ontology based IM
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

59. Agent-base IM
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.
Many other definitions …
Your own personal (digital) automatic assistant
knows about your preferences
builds up knowledge base using your past
can combine the local knowledge with remote services:
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

60. Intelligent Agents & Ontology
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

61. Agent: B2C Electronic Commmerce
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.
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

62. Example: Database Integration
Databases are very different in structure, in content
Lots of applications require managing several databases
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
how this semantics is mapped on internal structures is immaterial
Dickson Chiu 2011

63. Example: Digital Libraries
It is a bit like the search example
It means catalogs on the Web
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!
help you in finding the right publications
Dickson Chiu 2011

64. Content Management via Metadata
album pages
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?
artist bios
album reviews

65. Example Album Ontology
concert calendar
album pages
artist bios
TV listings
album reviews
Ontology
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66. Video Content Ontology Example
BSIM0012

67. Thank you! Question and Answer
Ontology
Dickson Chiu - update 2011