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Title: A Logical Framework for Exception Handling in ADOME Workflow Management System Author: Dickson Last modified by: Test Created Date: 6/2/2000 4:06:17 PM – PowerPoint PPT presentation

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Title: Metadata


1
Metadata  for Web-based Information Management
Dickson K. W. CHIU Senior Member, IEEE ACM Dickson Computer Systems Hong Kong kwchiu_at_acm.org, dicksonchiu_at_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
2
Towards a Semantic Web
  • WWW is an impressive success
  • amount of available information (gt 1 Giga-page)
  • number of human users (gt 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!)

3
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
  • etc.
  • Again, humans combine these information easily
  • even if different terminologies are used!

4
What is the Problem?
  • 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
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

6
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

7
Solution XML markup with meaningful tags?
ltnamegtWWW2002 The eleventh international world
wide webconlt/namegt ltlocationgtSheraton waikiki
hotel Honolulu, hawaii, USAlt/locationgt
How about ltconfgtWWW2002 The eleventh
international world wide webconlt/confgt ltplacegtSher
aton waikiki hotel Honolulu, hawaii, USAlt/placegt
Then how about lt??gtWWW2002 The eleventh
international world wide webconlt/??gt lt??gtSheraton
waikiki hotel Honolulu, hawaii, USAlt/??gt
8
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

9
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)

10
Ontology Origins and History
  • Ontology in Philosophy - a philosophical
    disciplinea 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.

11
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

12
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

13
Ontology Languages
  • RDF Schema
  • RDF is a data model for objects and relations
    between them
  • RDF Schema is a vocabulary description language
  • Describes properties and classes of RDF resources
  • Provides semantics for generalization hierarchies
    of properties and classes

14
Web Ontology Languages (2)
  • OWL
  • A richer ontology language
  • relations between classes
  • e.g., disjointness
  • cardinality
  • e.g. exactly one
  • richer typing of properties
  • characteristics of properties (e.g., symmetry)
  • Logic
  • BOTH are standards of www.w3.org

15
History of the Semantic Web
  • Web was invented by Tim Berners-Lee (amongst
    others), a physicist working at CERN
  • TBLs 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.
16
Adding Semantics
  • External agreement on meaning of annotations
  • E.g., Dublin Core (http//dublincore.org/)
  • 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

17
Berner-Lees Architecture
? Semanticsreasoning
?
? Relational Data
?
? Data Exchange
  • Relationship between layers is not clear
  • OWL DL extends DL subset of RDF

18
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

19
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

20
Motivation
  • Compare some general-purposed e-Marketplaces
    (auction based)
  • 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
  • Airtickets http//www.qunar.com/
  • Finding friends (!) http//hk.personals.yahoo.com
    /
  • Which one is better? Why?
  • Key issue gt capturing and applying domain
    knowledge

21
What is an e-Marketplace?
e
-
Marketplace
offers
Aggregate requests
Repository
from Buyers, contact
bids
potential Suppliers,
Ontologies and Concepts
match Suppliers
e
-
Negotiation data
and Buyers, exchange
offers
Agreements
-

bids and offers,
generate e
-
Contract
bids
Buyers
22
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
  • http//www.igi-global.com/journals/details.asp?id
    34268

23
Example Ontology Clothing and Sales Negotiation
24
Objective and Solution Approach
  • How to elicit negotiation requirements?
  • Semantic Web gt Ontologies
  • gt 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

25
Semantic basede-Marketplace Conceptual Model
26
Overall e-Negotiation Process Design Methodology
Requirements elicitation phase
Decision phase
27
Requirement Elicitation Methodology
  1. Traders select agreed ontology.
  2. Traders relate requirements to concepts in the
    selected ontology.
  3. 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.
  4. The system checks the consistency of all the
    concepts, issues, and their dependencies (Cheung
    et al. 2002).
  5. For a consistent plan, the system can proceed to
    elicit the possible alternatives otherwise we
    have to re-iterate from step 3.
  6. 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.

28
Decision Phase Methodology
  • The system
  • searches for the matching offers based on the
    traders 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.

29
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

30
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

31
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 -gt cost of
    production
  • Computational dependency
  • more obvious type of functional dependency
  • hardwired computational formula
  • E.g., insurance amount percentage cost of
    goods.

32
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

33
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

34
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 gt S, M, L,
    XL)
  • The elicitation of options is streamlined when a
    complicated issue is decomposed into
    concepts(appearance gt size color shapes)
  • Ontology provide
  • explicit ordering of them (size gt S lt M lt L lt
    XL)
  • implicit ordering
  • inheritance (is-a) hierarchies
  • composition hierarchies

35
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

36
System Implementation Architecture
37
OWL Listing
  • ltrdfrest rdfresource"http//www.w3.org/1999/02/
    22-rdf-syntax-nsnil"/gt
  • ltrdffirst rdfdatatype"http//www.w3.o
    rg/2001/XMLSchemastring"gtSmalllt/rdffirstgtlt/rdfL
    istgtlt/rdfrestgt
  • ltrdffirst rdfdatatype"http//www.w3.org/2001/X
    MLSchemastring"gtMediumlt/rdffirstgtlt/rdfListgtlt/rd
    frestgt
  • ltrdffirst rdfdatatype"http//www.w3.o
    rg/2001/XMLSchemastring"gtLargelt/rdffirstgtlt/rdfL
    istgtlt/rdfrestgt
  • ltrdffirst rdfdatatype"http//www.w3.org/
    2001/XMLSchemastring"gtExtra Largelt/rdffirstgtlt/rd
    fListgt
  • lt/owloneOfgtlt/owlDataRangegtlt/rdfsrangegt
  • lt/owlDatatypePropertygt
  • ltowlClass rdfID" UnitCost"gt
  • ltowlequivalentClassgt lt!-- unit cost depends
    on appearance --gt
  • ltowlRestrictiongt ltowlsomeValuesFrom
    rdfresource"Appearance" /gt lt/owlRestrictiongt
  • lt/owlequivalentClassgt
  • lt/owlClassgt
  • lt/owlOntologygt
  • ltowlOntology rdfabout"Clothing"gt
  • ltrdfscommentgtSample Clothing
    Ontologylt/rdfscommentgt
  • ltowlClass rdfID"Clothing" /gt
  • ltowlClass rdfID"Appearance" /gt
  • ltowlClass rdfID"Color"gt
  • ltrdfssubClassOf
    rdfresource"Appearance" /gt
  • ...
  • lt/owlClassgt
  • ltowlObjectProperty rdfID"hasAppearance"
    gt
  • ltrdfsdomain rdfresource"Clothi
    ng" /gt
  • ltrdfsrange rdfresource"Appeara
    nce" /gt
  • lt/owlObjectPropertygt
  • ltowlObjectProperty rdfID"hasColor"gt
  • ltrdfssubPropertyOf
    rdfresource"hasClothAppearance" /gt
  • ltrdfsrange rdfresource"Color
    /gt
  • ...
  • lt/owlObjectPropertygt
  • ltowlDatatypeProperty rdfID"size"gt lt!--
    Enumeration --!gt
  • ltrdfsdomain rdfresource"Appearance"/gt

38
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
Match-making 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
Match-making 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.
Recom-mendation Pre-set formulae for every type of product are needed for evaluation. Ontology help recommendation by evaluating offers in terms of flexible overall scaling
Recom-mendation 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.
Negotiation 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.
39
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

40
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

41
Question and Answer
Thank you! Email dicksonchiu_at_ieee.org
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