Transcript Document

The 2nd International Semantic
Web Conference (ISWC03)
20-23 october 2003
Sanibel Island (Florida)
Michele Missikoff
Federica Schiappelli
Summary
The conference program
Overview of the Semantic Integration
workshop
The main conference
Keywords for the Semantic Web
Roadmap (by Tim Berners Lee)
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The conference “events”
Tutorials
Workshops
Keynote speaches
Panels
Main conference
Posters presentation
Semantic web challenge
http://iswc2003.semanticweb.org/
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Tutorials
(on Monday, 20 oct.)
Agent-Mediated Semantic Web/Grid Services
Katia Sycara and Terry Payne
Tutorial on OWL
Peter F. Patel-Schneider, Ian Horrocks, and Sean Bechhofer
Creating Ontologies and Semantic Web
Applications with Protégé
Holger Knublauch and Natasha F. Noy
Information Integration on the World Wide
Web
Heiner Stuckenschmidt, Ubbo Visser, Holger Wache
http://iswc2003.semanticweb.org/pdf/Protege-OWL-Tutorial-ISWC03.pdf
http://webode.dia.fi.upm.es/iswc03/
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Workshops
(on Monday, 20 oct.)
1. Practical and Scalable Semantic Systems
2. Semantic Integration
3. Semantic Web Technologies for Searching
and Retrieving Scientific Data
4. Human Language Technology for the
Semantic Web and Web Services
5. Rules and Rule Markup Languages for the
Semantic Web
6. Evaluation of Ontology-Based Tools
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Summary
The conference program
Overview of the Semantic Integration
workshop
The main conference
Keywords for the Semantic Web
Roadmap (by Tim Berners Lee)
6
The information integration problem
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The Semantic Integration workshop
In the Semantic Web context, the information
are described by multiple ontologies and
schemas
Matching between ontologies and schemas is
still largely done by hand
Numerous research activities on methods for
describing mappings, manipulating them,
and generating them semi-automatically
Electronic proceedings: http://ceur-ws.org/Vol-82/
Invited talks (slides): http://smi.stanford.edu/si2003/invitedTalksAbstracts.html
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Keynote Speeches and Panels
(on Monday, 20 oct., during the workshop)
Semantic Web Scenarios involve rendez-vous
between peers
Requires mappings between their ontologies
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Generate mappings
Translate between ontology languages
Maintain mappings as ontologies change
The same problems as database schema
integration, BUT
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Current approaches to data management are not enough:
language-specific; problem-specific
Philip A. Bernstein Microsoft Research
Generic Model Management: A Database Infrastructure for Schema Manipulation
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A specific solution proposed by Bernstein
A generic approach: model management
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operators to manipulate models and mappings as bulk objects
 A model is a rooted directed graph, which represents a complex
information structure
 A mapping is a model that represents a transformation between two
models…
 …Or it could be a binary table (a morphism)
Schema matching (mapping discovery)
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Given two schemas, return correspondences that specify pairs of
related elements (lexical-structural-superclasses alignement)
Semantic Mapping
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Given correspondences between two schemas, return an
expression that translates instances of one schema into instances
of the other.
Model Merging
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Use the mapping to guide the merging
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Keynote Speeches and Panels
(on Monday, 20 oct., during the workshop) – cont’d
Two principal paths for info integration
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Using a central structure as ‘interlingua’
 Problems:
 Creating the central structure (coverage, consistency,
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updating)
Linking sources and targets to it (automatically?)
 Benefits:
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Linear (2N) in number of sources/targets
Creating individual source-to-target mappings
 Problems:
 Creating and updating the mappings (automatically?)
 N2 in number of sources/targets
 Benefits:
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Doesn’t require general one-size-fits-all model/structure
Edward Hovy Information Sciences Institute of the University of Southern California
Building Large Ontologies
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Hovy’s approach
The Interlingua route: toward a merged ontology
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General alignment and merging problem
Given many domain models—how can you integrate them
consistently, without overlap or redundancy?
Solution: Use a large general-purpose concept
network to provide the background—the SENSUS
Ontology. Improving alignment by enriching
content by adding definitional material and by
clustering entities.
The Transfer route: toward learning individual
mappings
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aligning databases directly
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Summary
The conference program
Overview of the Semantic Integration
workshop
The main conference
Keywords for the Semantic Web
Roadmap (by Tim Berners Lee)
13
Invited Speakers
(during the main conference)
Jim Hendler:
On Beyond Ontology: Returning to AI
from the Semantic Web
Michael Brodie:
The Long and Winding Road To Industrial
Strength Semantic Web Services
Tim Berners-Lee:
SemanticWeb:Where to direct our energy?
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Jim Hendler’s talk
 Director of a University of Maryland's lab; cochair of the W3C Web Ontology Working
Group
 his research group developed SHOE; creator of DARPA's DAML program
“…it's beginning to look like we may be successful! ”
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OWL, the Web recommended ontology language
Logic toolkits are produced by software companies
Time for us to think more about what we do with all this
Today Challenges:
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Integration… partial mapping
Process modelling … WSBPEL, OGSA, etc.
Temporal logic
OWL feeding
Common agreement for ontology building
Semantic Web technology available to vendors
Electronic proceedings: http://ceur-ws.org/Vol-82/
Invited talks (slides): http://smi.stanford.edu/si2003/invitedTalksAbstracts.html
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Michael Brodie’s talk
 Chief Scientist, Verizon Information Technology
 industrial researcher, focussing on advanced computational models and
architectures, the large-scale information systems that they support, business
and technical contexts
“Web Services: the basis for the Next Generation of
computing ! ”
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flexible
can be discovered and invoked anywhere
composed, as required, to achieve higher level goals
proposed to address software integration
Today Challenges:
 overcome the integration challenge on an industrial scale
 technical pragmatics such as scalability and performance
dominate
http://iswc2003.semanticweb.org/invitedtalks.html
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Tim Berners-Lee’s talk
 The Semantic Web inventor!
Speaking too fast…
didn’t understand anything…
See the conclusions!!!
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The main conference
Principal themes
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Foundations
Ontological reasoning
Semantic web services
Security, trust and privacy
Agents and the Semantic Web
Information Retrieval
Multi-media
Tools and methodologies
Applications
Industrial Track
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Summary
The conference program
Overview of the Semantic Integration
workshop
The main conference
Keywords for the Semantic Web
Roadmap (by Tim Berners Lee)
19
Interesting themes
Interoperability
Contexts
Languages: RDF(S); OWL
Reasoning with DL
Web services…composition?
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Interoperability
Semantic coordination
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(solution by Trento univ.)
Semantic models considered are hierarchical
classifications, represented as labelled graphs
Logical formulae are built taking into consideration
 lexical knowledge (words in labels),
 domain knowledge (relations bw concepts represented by
labels),
 structural knowledge (isa hierarchy).
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Shift from computing linguistic and structural similarity
to the problem of deducing relations bw sets of
logical formulae, encoding the meaning of the
involved entities (nodes on the graph)
P. Bouquet University of Trento
Semantic Coordination
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Contexts
Context is a model of some domain,
which encode a particular view
Context is local (reduced sharability)
Mapping among contexts is the issue
Contextual Ontologies =
Ontology + context mapping
Fausto Giunchiglia University of Trento
C-OWL: Contextualizing Ontologies
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Languages: RDF(S)
RDF(S) has a non standard metamodelling architecture
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Multiple modelling primitives seem to be
represented by the same RDFS primitive (e.g.
rdf:type, rdf:subClassOf)
A Fixed meta-modelling architecture has been
proposed
I.Horrocks F.Patel-Schneider University of Manchester
RDFS(FA) and RDF MT: two semantics for RDF
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A non standard meta-modelling
architecture
RDF(S) is used to add metadata annotations to Web res.
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Subject-predicate-object triples used to link resources
i.e., triples represent knowledge about domain (such as Federica worksWith
Francesco)
Federica
Francesco
worksWith
RDF(S) also used to define syntax and semantics of
subsequent language layers (and even of itself), e.g.:
Parent
Restriction
hasChild
subClassOf
equivalentClass
onProperty
subClassOf
subPropertyOf
minCardinality
1
subClassOf
Class
Resource
type
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Problems with RDF MT
Not clear that RDF(S) is appropriate for both
functions (at once)
Uniform semantic treatment of triple syntax
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i.e., “syntax” and “knowledge” triples have same
semantics
I use
Confusing (for some) cyclical Should
meta-model
owl:Class or rdfs:Class?
Semantics given by “non-standard” Model
Theory
instance of rdfs:Class
More expressiverdfs:Resource
ontology
languages layered
rdfs:Class subclass of rdfs:Resource
…Resource is instance of its subclass??
on top of RDF(S)
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E.g., OIL, DAML+OIL, and now OWL
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RDFS(FA)
RDFS(FA) is a sub-language of RDF(S)
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It stands for “RDFS with Fixed layer metamodeling Architecture”
Has a First Order/Description Logic style semantics
The universe of discourse is divided up into a
series of strata
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User defined facts, vocabulary and RDF/OWL builtin vocabulary are (typically) in different strata
Each modelling primitive belongs to a certain
stratum (layer)
 Labelled with different prefix to indicate the stratum
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RDFS(FA) layers
Stratum 3 (Meta-Language Layer)
fa:MResource,
fa:MClass
fa:MProperty …
Stratum 2 (Language Layer)
fa:LResource,
fa:LClass
fa:LProperty …
Stratum 1 (Ontology Layer)
fa:OResource
Person, Researcher
workWith …
Stratum 0 (Instance Layer)
Federica, Francesco…
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Advantages of RDFS(FA)
No problems layering FO languages on top of
RDFS(FA)
RDFS(FA) supports use of meta-classes and
meta-properties
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In stratum above classes and properties
RDFS(FA) metamodel very similar to that of
UML
Possible to define a new sub-language of
OWL: OWL FA
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Extends OWL DL with meta-classes/properties
Fully compatible with OWL DL semantics
Reasoning (even for meta-classes/properties)
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Reasoning with DL
Reasoning with ontology languages is
important to exploit the semantics of ontologybased annotations
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Instance checking
Subsumption (taxonomic) reasoning
Used in SW applications
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E.g. search engines, matchmaking of services,
document classification, etc…
OWL is strictly related to Description Logics
DL provides such reasoning facilities
I.Horrocks F.Patel-Schneider University of Manchester
Reducing OWLentailment to DL satisfiability
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Web services (?)
Karlsruhe:
WS Composition is a planning problem or
pre-/post-cond matching
OntoMat-Service (tool for WS workflow)
S.Agarwal, S.Handschuluh, S.Staab University of Karlsruhe
Surfing the Service Web
BPEL4WS (Stanford Univ.)
Coreography Fwk
BPEL (programming lang) to specify the
sequence of tasks
Partner selected at runtime
Automatic semantic translation
D.J.Mandell, S.McIlraith University of Stanformd
Adapting BPEL4WS for teh SW: the bottom-up approach to ws interoperation
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Summary
The conference program
Overview of the Semantic Integration
workshop
The main conference
Keywords for the Semantic Web
Roadmap (by Tim Berners Lee)
31
SW status
OWL becomes stable
Steadily growing deployment of
RDF
Growing SWeb-specific industry
sector
SW Services starting to take off
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Risks
Architecture becomes fractured,
weak, or baroque
Fracture between Web and S/Web arch
Fragmentation in query and rules
RDF/XML syntax shock
Perceived relationships between SW
and WS
Deployment in real products
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The Killer App for the Semantic Web
It’s the integration!!!
Guidelines
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Be careful of terms - ontology, semantics, etc
Explaining how communities interact
Please re-use
 Don't create new URI schemes
 Don't re-invent HTTP space
 Don't re-invent RDF
 Don't re-invent ontologies where they exist
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Where to direct our energy?
Indexing data - by ontology
Indexing rules, building translation
paths
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like one big database? or one big web?
SW and WS
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Discovery should be SemWeb-based
Balances
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Engineering vs Research
Getting it working vs getting it right
Tractable Machinery vs Heuristics
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Thank you for the attention
Mmm mm
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