Querying Complex Information

1
Querying Complex Information

• Doctoral School
• April 2007
• Fabio Porto

2
Objective

• This course compiles the work Ive being
  developing on modeling and querying
  ontology-based applications

3
Agenda

• WSMO,WSML Overview
• Semantic Web Service Discovery
• Querying multiple ontologies

4
QoS-enabled Semantic Web Service Discovery
5
Objective

• To develop a semantic approach for finding
  published web services based on high level user
  queries (goals)
• Includes
• Semantic description of web services
• Model for quality of services
• Matchmaking (between goals and WS descriptions)
• Ranking

6
Context

• Project developed as part of the DIP FP6
  Integrated Project supported by the European
  union
• DIPs objective was to develop and extend
  Semantic Web and Web Service technologies in
  order to produce a new technology infrastructure
  for Semantic Web Services (SWS)
• Project ran from 2004 to 2006

7
Project outcomes

• WSMO model
• WSML language in different variants
• WSMX execution environment
• Tools editing, repositories,discovery,

8
Roadmap

• WSMO Overview
• Discovery in DIP
• Introduction on QoS-enabled Discovery
• QoS modelling
• QoS-based matchmaking
• Conclusion

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1. WSMO Overview

• Adapted from - WSMO TutorialMichael Stollberg,
  Titi Roman, Holger Lausen
• http//stadium.open.ac.uk/stadia/preview.php?which
  event542s35schedule533

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WSMO Top Level Elements
Objectives that a client may have when consulting
a Web Service
Provide the formally specified terminology of the
information used by all other components

• Semantic description of Web Services
• Capability (functional)
• Interfaces (usage)

Connectors between components with mediation
facilities for handling heterogeneities
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Layered Architecture
Trust
Web Service Description
Mediator
Goal
Domain Ontology
WSML
WSMO
Web Service
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WSMO 1.0 Ontologies

• Used as data model throughout WSMO
• Ontology elements concepts, relations,
  functions, axioms and instances
• Ontology Specification Language WSML
• Web Compatibility
• Namespaces
• WWW Identification Concept (URI, Literal,
  Variable)
• Basic Datatypes from XML Schema

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WSMO 1.0 Goals

• De-coupling of Request and Service
• Objective description independent of service
  usage
• inherent support for discovery service usage
• Constituting description elements
• Post-condition object of interest (computational
  aspects)
• effect conditions that have to hold after
  resolution (real world aspects)
• gt Only objective specification without regard to
  resolution by service
• Usage
• Goal Ontologies (pre-existing Goal Templates)
• Goal Resolution Process open to implementations
  

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WSMO 1.0Web Services

• complete item description
• quality aspects
• Web Service Management

• Advertising of Web Service
• Support for WS functional Discovery

Capability functional description
Non-Functional Properties Core WS-specific

• Realization of WS by using
• other Web Services
• Functional
• decomposition
• WS
• Composition

Web Service Implementation (not of interest in
Web Service Description)

• Interaction Interface
• for consuming WS
• Messages
• External Visible
• Behavior
• Grounding

Choreography --- Interfaces ---
Orchestration
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WSMO 1.0Web Service Capability

• Non-Functional Properties
• Imported Ontologies
• Used Mediators
• OO Mediator importing ontologies as terminology
  definition
• WG Mediator link to a Goal that is solved by
  the Web Service
• Pre-Conditions
• Input with conditions that web service expects
  in order to be able to provide its service
  (computational aspects)
• Assumptions
• Conditions that have to hold before the Web
  Service can be executed
• (real world aspects)

before execution
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Web Service Capability

• Post-Conditions
• Result / Output of Web Service in relation to
• the input, and conditions on it (computational
  aspects)
• Effects
• Conditions / Changes on the state of the world
• that hold after execution of the Web Service
  (real world aspects)

after execution
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WSMO Service Example (1)
A WSMO Ontology
Namespace tclthttp//www.wsmo.org/ontologies/tripR
eservationOntologygt .. Ontology
lthttp//www.wsmo.org/ontologies/tripReservationOnt
ologygt nonFunctionalProperties .
endnonFunctionalProperties concept route
nonFunctionalProperties dcdescription
hasValue Route between two stations
endNonFunctionalProperties startLocation
ofType tcstation endLocation ofType
tcstation concept reservation
nonFunctionalProperties dcdescription
hasValue Reservation with a reservation owner
endNonFunctionalProperties
reservationNumber ofType xsdinteger
reservationRoute ofType route
reservationHolder ofType prsperson
A WSMO Service
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WSMO Service Example (2)
Service Capability
Service Choreography
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WSMO 1.0Mediators

• For handling heterogeneity
• Mediator Types OO, GG, WG, WW

WSMO Mediator uses a Mediation Service via
Source Component
Target Component
1
1 .. n
Source Component

• as a Goal
• directly
• optionally incl. Mediation

Mediation Services
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WSMO 1.0OO Mediator Example
Merging two ontologies
Train Connection Ontology (s1)
OO Mediator Mediation Service
Train Ticket Purchase Ontology
Purchase Ontology (s2)
Goal merge s1, s2 and s1.ticket subConceptOf
s2.product
Discovery
Mediation Services
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WSMO 1.0Non-Functional Properties

• Every WSMO element is described by properties
  that contain relevant, non-functional aspects of
  the item
• Core Properties (for every WSMO element)
• Dublin Core Metadata Set
• Version
• Service Specific Properties
• Quality of Service Information
• Financial / contractual properties

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WSMO 1.0nfp Core Properties
ontology lt"http//www.wsmo.org/ontologies/trainCon
nection"gt nonFunctionalProperties dctitle
hasValue "International Train Connections
Ontology" dccreator hasValue "DERI
International" dcsubject hasValues "Train",
"Itinerary", "Train Connection", "Ticket"
dcdescription hasValue "International Train
Connections" dcpublisher hasValue "DERI
International" dccontributor hasValues
"Michael Stollberg", lt"http//homepage.uibk.
ac.at/C703225/foaf.rdf"gt,
lt"http//homepage.uibk.ac.at/c703240/foaf.rdf"gt,
lt"http//homepage.uibk.ac.at/c703262/foaf.rd
f"gt dcdate hasValue "2004-10-08" dctype
hasValue lt"http//www.wsmo.org/2004/d2ontologies"
gt dcformat hasValue "text/html"
dcidentifier hasValue lt"http//www.wsmo.org/ontol
ogies/trainConnection"gt dcsource hasValue
lt"http//www.wsmo.org/2004/d3/d3.3/v0.1/20041119/r
esources/tc.wsml"gt dclanguage hasValue
"en-US" dcrelation hasValues
lt"http//www.daml.org/2001/06/itinerary/itinerary
-ont"gt, lt"http//daml.umbc.edu/ontologies/itta
lks/person"gt, lt"http//www.wsmo.org/ontologie
s/dateTime"gt, lt"http//www.wsmo.org/ontologie
s/location"gt, lt"http//www.daml.org/2001/02/ge
ofile/geofile-ont"gt, lt"http//www.daml.org/200
1/02/geofile/geofile-ont"gt dccoverage
hasValue "ID7029392 NameWorld" dcrights
hasValue lt"http//www.deri.org/privacy.html"gt
version hasValue "Revision 1.6 "
endNonFunctionalProperties
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WSMO 1.0nfp Service Specific Properties

• Quality Aspects and other non-functional
  information of Web Services
• Accuracy Robustness
• Availability Scalability
• Financial Security
• Network-related QoS Transactional
• Performance Trust
• Reliability

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WSML Overview
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Web Service Modeling Language

• Four elements of WSMO
• Ontologies, Goals, Web Services, Mediators
• WSML provides a formal grounding for the
  conceptual elements of WSMO, based on
• Description Logics
• Rule Languages
• First Order Logic

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WSML Rule

• WSML distinguishes between a conceptual and a
  logical expression syntax.
• The conceptual syntax is used for the modeling of
  web services, goals, mediators and ontologies.
• The logical expression syntax is used for the
  specification of axioms and constraints in an
  ontology and inside the pre- and post-conditions
  of goals and web services.

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WSML-Rule

• Based on Logic Programming-variant of F-Logic and
  HiLog
• Minimal model semantics
• Implements default negation
• Allows unrestricted use of function symbols
• Full support for goal/web service modeling

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QoS Base example
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Functional Discovery
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Overall Discovery Process
Requester Desire
Goal-Repos.
Ease of description
Predefined formal Goal
Goal Discovery
Selected predefined Goal
Goal refinement
Requester Goal
Available WS
Efficient Filtering
Abstract Capability
Web Service Discovery
Service Discovery
Concrete Capability (possibly dynamic)
Accuracy
Still relevant WS
Service to be returned
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Discovery Techniques

• Aim of Discovery detect suitable Web Services to
  solve a Goal
• different techniques usable
• Key Word Matching v
• match natural language key words in resource
  descriptions
• Controlled Vocabulary v
• ontology-based key word matching
• Logical Semantic Resource Descriptions v v v
• what WSMO aims at

Ease of provision
Possible Accuracy
32
Matchmaking Notions

• Exact Match
• G, WS, O, M ?x. (G(x) ltgt WS(x) )
• PlugIn Match
• G, WS, O, M ?x. (G(x) gt WS(x) )
• Subsumption Match
• G, WS, O, M ?x. (G(x) lt WS(x) )
• Intersection Match
• G, WS, O, M ?x. (G(x) ? WS(x) )
• Non Match
• G, WS, O, M ?x. (G(x) ? WS(x) )

G
WS
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QoS-Enabled Semantic WS Discovery

• Our contribution on D4.17,D4.18,D4.19
• In collaboration with
• Le-Hung Vu,
• Manfred Hauswirth, Othman Tajmouati, Sebastian
  Gerlach

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Motivation

• QoS is critical for users in business
  applications
• Availability of the service
• Execution time
• Response time
• Robustness error and exception handling
• Accuracy of the results (computational services)
• and also a key to business success
• Freshness/Coverage (business information
  services)
• Timely delivery (online book-stores)
• Quality of food (online-order restaurants)
• Quality of accommodation (hotel reservation
  services)
• Quality will enable the ranking of Web service
  search results in the case of many functionally
  equivalent services(compare to Web search!)

Network/ Software-related QoS parameters
Application-level QoS features
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QoS-based WS discovery

• QoS-based WS Matchmaking
• WS providing the same functionality (resulting
  from functional matchmaking)
• e.g. services providing Stock Market news
• are further filtered out through QoS matchmaking
  and ordered by QoS-based ranking
• e.g. differences in Data freshness
• QoS-based ranking
• Service providers may exaggerate their advertised
  QoS ratings for attracting more customers
• e.g. inaccurate data freshness values
• Users may cheat when rating QoS
• ? A reputation-based QoS ranking provides some
  guarantee that a customer is not misled by false
  QoS claims

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Motivation Example
Get Stock Headlines
Get Stock Headlines data freshness 5
min and availability gt 0.99 and price lt 100
euros
price lt 100 euros
data freshness 1 min availability 0.9999
price200 euros
price200 euros
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Motivation Example
Trust/Reputation
Weird, Verireputation indicates data freshness
6 min
Xignite/
WSGetNews
WSGetNews
data freshness 5 min availability 0.9999
price20 euros
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Goals (in DIP)

• Model web service QoS
• Specify semantic matchmaking and ranking based on
  the QoS model
• Specify and implement Reputation Assessment
  Mechanisms
• Design a scalable solution

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QoS definition

• QoS in DIP is the set of non-functional
  characteristics and environmental conditions that
  distinguishes a service from others offering the
  same functionality
• QoS in Semantic Web Service Discovery describes
• Quality characteristics of concrete services
• How fresh stock market information is?
• Quality characteristics of Web services
• Is the web service available 90 of the time?

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QoS differ individual services
In theory, a QoS specification should distinguish
individual services, such that a value can be
associated to each one of them,
QoS Description
Functional Description
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3. QoS Modelling
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QoS Overall Model
QoS upper ontology
is the basis for
User QoS Domain Ontology
Service QoS Domain Ontology
ranking ontology
used in
used in
Goal QoS spec. Ontology
WService QoS spec. Ontology
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Qos Modeling

• QoS Upper ontology
• QoS price model
• QoS concepts model
• QoS Environmental concepts
• QoS comparison model
• QoS measurement Model
• Relationship between QoS and Environmental
  concepts
• QoS Domain ontology
• Extends basic Upper ontology definitions
• Ex QoS model for financial market, tourism,
• QoS on service description and Goals
• QoS instances
• Environmental criteria

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QoS Upper Ontology
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QoS Upper ontology components

• Price model
• Specify price unit
• Price rules
• Conversion functions
• Relationship between the price concept and other
  QoS concepts
• Comparison Model
• Comparison criteria between QoS Concepts
• Higher better, lower better
• Inclusion
• Similarity,
• Measurement model
• Measurement units
• Conversion rules

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Snippet of QoS Upper Ontology
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Web Service and Goal QoS Model

• Composed of two parts
• QoS criteria
• Express the space of QoS values accepted/required
  by the Web service or goal, respectively (Quality
  level)
• A constraint over QoS concept instances
• A formula of the type (X1? C1) (X2? C2)
  (Xn? Cn)
• Where CI is a constraint over instances of CI, a
  QoS concept, and Xi is either ( if is
  optional, or false otherwise)
• Example
• Throughput gt 1MB/s and fileSize lt 100MB
• Environmental criteria
• Specifies conditions the user must agree in other
  to be eligible for a certain quality level
• It is expressed similarly to a QoS criteria
  formula
• Example
• LocationLausanne and linkwireless

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QoS specification in WSML

• Defined per interface
• Specified as a separate ontology imported by the
  web service or goal interface
• Work around to fit within current WSMO NFP
  limitations
• QoS ontology in WS and goal can import domain
  specific QoS ontologies

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QoS in WSML
Goal/WS description
1..n
Interface desc.
1..1
0..1
Required capability
QoS Spec
0..n
0..n
Environmental specification
QoS Value spec
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Snippet of a WS QoS Spec. ontology
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Ranking ontology

• Specifies
• Ranking model
• Associate values to matching result
• Matching thresholds
• User preferences
• Users weight differently QoS Concepts
• Reputation information
• User relevance for reputation info

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Code snippet ranking ontology
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4. QoS-based matchmaking
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QoS Overall Model with matching
QoS upper ontology
is the basis for
Domain QoS ontology
Domain QoS ontology
ranking ontology
used in
used in
SWS description ontology
Goal description ontology
compose Knowledge Base
matchmaking
QoS service discovery
ranking
55
Matchmaking Model
For matching
Matching is
For QoS concepts
For environment
56
QoS-based matchmaking
WS description
Goal description
1..n
1..1
Interface desc.
Interface desc.
0..1
1..1
1..1
0..1
Required capability
QoS requirement
Provided capability
QoS offering
0..n
0..n
0..n
0..n
Environmental specification
QoS required spec
environmental requirement
QoS provided spec
contextual matchmaking
QoS parameter matchmaking
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QoS-based Service Ranking

• The QoS-based ranking of a service depends on
• Compliance (i.e. match) of services QoS
  definition with users QoS goals (QoS ontology)
• Reputation-based estimates for services QoS
  attributes
• User preferences QoS attribute aggregation
  (Ranking ontology)
• The reputation-based QoS estimation depends on
• QoS ratings reported by users
• QoS ratings reported by Trusted Agents (e.g.,
  AlertSite, Dot-Com Monitor, Empirix)
• QoS ratings advertised by the service providers
• This work is among the first ones applying
  reputation-based mechanisms for QoS-based service
  discovery VHA05
• The reputation-based QoS ranking is compliant
  with the more general trust model developed in
  D4.21.

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Dealing with partial matching

• When comparing two QoS instances (QoS or
  environment concept) the comparison model
  associates a value within a matching range
• We define in the user QoS ontology matching
  thresholds
• Upper bound eh
• lower bound el min( (1- eh ), eh )
• For 0 e 1

0
el
eh
1
Not match
exact match
partial match
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Example
Weight w10, w21, w31 el eh0.5
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5. QoS-based discovery framework and architecture
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QoS-based service discovery framework

• The whole QoS-based discovery framework is
  modeled in term of algebraic operators which
  enables plugging-in of different implementations
• ? Highly customizable implementation

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QoS Discovery Architecture

• QoS discovery architecture
• QoS discovery component
• QoS matchmaking
• Ranking based on QoS
• Functionality implemented as algebra operators
• DIP discovery API
• Derby DBMS stores
• WSMO collections
• QoS estimates

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QoS Discovery Architecture

• Reputation
• Computes reputation estimates
• Apply discovery over computed estimates

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QoS Discovery Architecture
QoS
Reporter

• Parallel Discovery
• Scales-up with parallel matchmaking
• Efficient adaptive parallel evaluation

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Query Execution Plan for Processing Service
Discovery Requests
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Conclusion

• A new model for QoS in SWS
• First work to introduce QoS in DIP/WSMO!!
• A matchmaking and ranking approach
• Is completely configured on a domain and
  user/provider basis
• Including configuration of
• Domain ontology
• Ranking ontology
• Preferences
• It is completely implemented
• More information
• QoS-enabled Semantic Web Service Discovery a
  Personalized approach, submitted to IDEAS 2007

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68
Reasoning on Dynamically Built Reasoning Space
with Ontology Modules

• Fabio Porto
• École Politechnique Fédéral de Lausanne
• Database Laboratory
• Switzerland

69
Outline

• Introduction
• Preliminaries
• General Framework
• Reasoning space
• Reasoning algorithm
• Conclusion and Future work

70
Introduction

• Ontologies are increasingly used as explicit
  models of the conceptualization of underlying
  information sources
• Different ontologies
• representing partially intersecting domains
• same domain observed from different perspectives
• Applications require reasoning over such
  autonomously developed ontologies

71
Multiple ontology scenarios

• In e-science
• In order to study colon carcinoma disease, a
  biologist would conjointly use its own ontology
  with others such as
• medical ontology (UMLS), anatomical
  ontology(mouse ontology, HUMAT), Pharmacogenomics
  (PharmGKB) and GO
• In e-business
• In automatic web service discovery within a
  virtual travel agency
• Location ontology, currency ontology, flight
  ontology, date ontology

72
Overall picture
O3
O1
O2
Group ontology
73
Context

• User group agrees on a common ontology but trusts
  on information defined on other ontologies
• Autonomously developed ontologies
• Partially intersect
• Represented through different logical languages
• Require mappings among ontologies models
• May include contradictions
• Current reasoners
• Consider ontologies forming a single logical
  model
• Need different ontologies to be aligned
• Centralized Reasoning
• Transferring large ontologies to a central site
  is costly

74
Problem Statement

• Given a set of autonomously developed ontologies
  linked by mapping correspondences to a group
  ontology, conceive a reasoning strategy
  compatible with current centralized reasoners
• Contribution
• Strategy to dynamically build a reasoning space
  based on a query, an ontology space and
  correspondences

75
Preliminaries

• Ontologies expressed in SHIQ
• Distinct sets of concepts, roles and
  individuals
• ?, ? are concepts and, if C,B are concepts, then
  ?C, ( C ? B), (C ? B) are concepts
• An ontology O is modeled by an interpretation I.
• I(?I, (I))
• Concept names are interpreted as subsets of ?I
• Complex expressions are interpreted according to
  the following equations
• ? I ?I ? I ? ( C ? B)I (C I? BI) ( C ?
  B)I (C I? BI)
• ?C I ?I \ C I
• Knowledge Base
• ( C ? B), ( R ? S), aC, lta,bgtR
• Interpretation
• ( C ? B) iff ( C I ? B I )
• aC iff aI ? C I

76
General Framework

• Ontology Space OSO1, O2,, On, where Oi is an
  ontology
• Ontology Module Midltid,D,L,Ob,Cid,OSgt
• C correspondences(bridge rules in c-owl)
• OiC OjD OiC ? OjD OiC ? OjD OiR
  OjS
• Oiv Ojt
• Where C, D are concepts R, S are roles and v,t
  are instances Peer PiltMi,Sgt
• Query
• Q q1 ? q2 ? ? qt. Horrocks,Tessaris 2000
• qi,1 i t, is a term xC or ltx,ygtR
• qi is satisfied by O iff O? qi
• Boolean queries
• C ? D
• Peer PltM,Reasoner,query rewritergt

77
Reasoning Space

• Virtual ontology built to answer a query Q with
  relevant entities from OS
• RS ? OS ? C
• How to compute relevant entities (Q,OS)?
• Relevant entity e in Oi
• ei ? Oi is relevant to Q iff ? ej in qj ,term
  of, Q, such that
• ei ? ej? ?

78
Reasoning Space

• Virtual ontology built to answer a query Q with
  relevant entities from OS
• RS ? OS ? C
• How to compute relevant entities (Q,OS)?
• Relevant entity e in Oi
• ei ? Oi is relevant to Q iff ? ej in qj ,term
  of, Q, such that
• ei ? ej? ?

79
Building Reasoning Space

• Successively extend the Reasoning space by
  identifying relevant ontology entities
• Initially assumes RSlocal ontology
• Apply a reasoning space extension function that
  for each ontology in OS, identifies relevant
  entities
• Reason over the current RS

80
Getting relevant entities
Bodily Process
xProtein ? (x,lactation)involved_with
? (x,disease)involved_with
Lactation
digestion
Involved_with
Protein
Disease process
Receptor Protein
Breast Cancer
Involved_with
Stomach Cancer
81
Getting relevant entities
Bodily Process
xProtein ? (x,lactation)involved_with
? (x,disease)involved_with
Lactation
digestion
Involved_with
Protein
Disease process
Receptor Protein
Breast Cancer
Involved_with
Stomach Cancer
82
Extending RS
xProtein ? (x,lactation)involved_with
? (x,disease)involved_with
Bodily Process
Lactation
Involved_with
Protein
Disease process
Receptor Protein
Breast Cancer
Involved_with
83
General Framework
Are there proteins involved in lactation and
disease processes?
xProtein ? (x,lactation)involved_with
? (x,disease)involved_with
84
Reasoning space algorithm
reasonspace(query Q,ontology Ob,OS,correspondence
C) answer RS ObRS?
q ?i1,t qt / qt terms of query Q /
answerevaluate(q,RS) qq
satisfied(q) While (q ?? and RS ?
RS) RSRS RS
f(q,RS,OS) answer answer ?
evaluate(q ,RS) qq
satisfied(q) return answer
85
Related work

• L. Serafini, A Tamilin, Distributed reasoning
  services for multiple ontologies. Technical
  Report DIT-04-029, University of Trento, 2004
• M. Lenzerini,Data Integration A Theoretical
  Perspective, ACM PODS 2002
• D. Calvanese, G. De Giacomo, M. Lenzerini, and R.
  Rosati, Logical foundations of peer-to-peer data
  integration, PODS 2004
• E. Fancioni, G. Kuper, A. Lopatenko, L. Serafini,
  A Robust Logical and Computational
  Characterisation of peer-to-peer Database
  systems, DBISP2P-2003, co-loacated VLDB2003
• K. Alberer, P. Cudré-Mauroux, M. Hauswirth,
  GridVine Building Internet-Scale Semantic
  Overlay Networks , The 3rd International
  Semantic Web Conference (ISWC2004), Hiroshima,
  7-11 Nov 04
• J.Lin and A. O Mendelzon. Merging databases under
  constraints, Intl. J. of Cooperative Information
  Systems, 7(1)55-76,1988.
• A. Halevy et al., Z.G. Ives, D. Suciu,
  I.Tatarinov. Schema mediation in peer data
  management systems. ICDE 2003.
• L. Serafini, F. Giunchiglia, J. Mylopoulous, P.
  Bernstein. Local relational model A logical
  formalization of database coordination. In
  context 2003, 2003.

86
Conclusion and Future work

• Preliminary results on reasoning over
  autonomously developed distributed ontologies
• Present a strategy that
• Uses current reasoner technology
• Reduces the cost associated to transferring
  ontologies
• Identifies contradictions among ontologies
• Is based on database approach for evaluating
  distributed queries
• Future work
• Use query results to update mapping information
• Evaluate the approach comparing to distributed
  reasoning strategies based on distributed tableau
  method
• Evaluate quality of results
• Conceive an approach for determining which peer
  to ask

87

• Thank You !!!

88
4. Reputation-based QoS ranking
89
QoS Specification in WSMO Web Service

• webService FileHostingService
• capability .
• interface freeService
• nfp dcrelation hasValue
  freeServiceserviceSLA endnfp
• interface subscribedService
• nfp dcrelation hasValue
  subQoSserviceSLA endnfp
• axiom freeServiceserviceSLA definedBy
• serviceSLA(any,qosyes) impliedBy
  ?userExecEnvironmentqoshasNetworkConnectionSpeed
  hasValue ?networkUser,
  qoshasLocation hasValue ?locationUser memberOf
  qosUserExecutionEnvironment
• and qosenvRequirementMatch(?networkUser,requ
  iredNetworkConn,qosexactMatch) and
  qosenvRequirementMatch(?locationUser,qosSwitzerl
  and,qosexactMatch) .
• instance serviceSLA --
• instance providedUploadSpeedSLA
• instance providedNumberConcurrentUploadsSLA

90
Specification of QoS Requirements in WSMO Goal
Description

• Goal RequiredFileHostingService
• capability
• interface
• nfp dcrelation hasValue
  goalQoSsatisfiesQoS endnfp
• axiom goalQoSsatisfiesQoS definedBy
• satisfiesQoS (fileHostingQoSUploadSpeed,
  qosyes) impliedBy
• ?serviceQoSSpecfileHostingQoShasUploadSpeed
  hasValue ?serviceUploadSpeed memberOf
• fileHostingQoSFileHostingServiceQoSSpecificatio
  n and
• qosqosRequirementMatch(?serviceUploadSpeed,reqU
  ploadSpeed,qosexactMatch) .
• satisfiesQoS (fileHostingQoSNumConDownloads,
  qosacceptable) impliedBy
• satisfiesQoS (fileHostingQoSSupportFileSize,
  qosyes) impliedBy
• instance reqUploadSpeed memberOf
  fileHostingQoSUploadSpeed
• instance reqNumConUploads memberOf
  fileHostingQoSNumberConcurrentUploads
• instance reqSupportFileSize memberOf
  fileHostingQoSSupportFileSize

91
QoS Modelling
QoS upper ontology
is the basis for
Web service QoS ontology
Goal QoS ontology
ranking ontology
used in
used in
SWS description
Goal description
compose Knowledge Base
matchmaking
QoS service discovery
ranking
92
QoS Modelling
QoS upper ontology
is the basis for
Web service QoS ontology
Goal QoS ontology
ranking ontology
used in
used in
SWS description
Goal description
compose Knowledge Base
matchmaking
QoS service discovery
ranking