Title: The Semantic Web: Current Status and Challenges
1The Semantic WebCurrent Status and Challenges
- Stefan Decker
- Information Sciences Institute, University of
Southern California - Digital Enterprise Research Institute, National
University of Ireland, Galway - and collaborators
- Sergey Melnik, Wolfgang Neijdl, Bijan Parsia,
Mario Schlosser, Michael Sintek
2Ho! what have we here
- So very round and smooth and sharp? To me 'tis
very clear This wonder of an Elephant Is very
like a spear! - John Godfrey Saxe (1816-1887)The Blind Men and
the Elephant
3Outline
- Semantic Web Overview
- Standards
- RDF
- Ontologies
- Research
- Rules
- Web Services Routing in P2P Networks
- Conclusion
4Semantic Web
- coined by Tim Berners-Lee (1997)
- "The Semantic Web is an extension of the current
web in which information is given well-defined
meaning, better enabling computers and people to
work in cooperation. - T. Berners-Lee, J. Hendler, O. Lassila,The
Semantic Web, Scientific American, May 2001
5Motivation Why do we need the Semantic Web?
- Overabundance of Information
- Highly scattered and distributed
- Need to search and integrate information
- Cost for locating relevant information and
deriving value from it is prohibitively
expensive - Reduce costs by
- Interconnecting workflows and business processes
- Enable data and service sharing
- Enable collaboration and information sharing
- Data and services sharing between diverse
scientific groups such as genomic and biological
sciences, and geosciences - Across industrywide consortiums and standards
bodies
6How do we get there?
Research communities
DL, AI, DB, NLP, Networking
Standards bodies
W3C, OMG,
Non-profit
US, EC, (Japan?)
Industry
IBM, Nokia, HP, Microsoft(?),...
Business.semanticweb.org
7Means to Achieve the Vision
- Explicit Ontologies and Interoperable Data
- Needed to understand each others data
- Web Services
- Required to actively interconnect
systems(automatically make an appointment)
8Technical challenges
- Interoperability
- Inaccurate, incomplete, heterogeneous data
- Unreliable, ill-defined, evolving services
- Natural language processing, data mining
- make information explicit
- Human-computer interaction
- querying interfaces, visualization
- Scalability
- Subsecond performance
9Social challenges
- Standardization is hard
- DublinCore
- Bogus or inaccurate metadata
- Physician rating, profile
- Competition and commoditization
- Economical incentive
- Chicken and egg
- Complexity developers and users
10Its the Economy, Stupid!
- PapiNet.org
- Vocabulary for Paper Industry
- BPMI.org
- Vocabulary for exchanging Business Process
Models - XML-HR
- Vocabularies for human resources (HR)
- DMTF Distributed Management Task Force
- Vocabularies for managing enterprises
- Wide range of E-Business standards available at
- http//www.oasis-open.org/home
11Its the Knowledge, Stupid!
- Gen Ontology Working Group
- Annotations of Gene Sequences
- Earth Sciences (SCEC, GEON)
- Exchange of geology and earthquake data
- MeSH
- Annotations of medical research literature
(MEDLINE) - Bio-informatics
- Micro-array data markup language
- UMLS Metathesaurus
- Integration of Biomedical Vocabularies
- http//umlsinfo.nlm.nih.gov
12Its the People, Stupid!
- E-commerce didnt invent the web, the web
invented E-commerce - Case Friend of a Friend (FOAF)
- Ontology driving community
- Trust-based models for
- Information and Semantics
- E-Commerce (E.g., E-bay)
- A little semantics, in the right place at the
right time, can excite people - But, the gain must be worth the pain
- Must have perceived end value
- The journey must seem not to horrible
13Technology
- The Resource Description Framework
- Ontologies
- Rules
- Web Services/P2P Networks
14Heterogenous Data
- To many data formats/languages
151. Step
- Define uniform, underlying syntax
- Lowest common denominator labeled
graphs(semi-structured Data) -gt RDF
Relational Database
Structured Text (e.g., Vcard)
Person
begin vcardfn Stefann
DeckerStefanend vcard
Person
row
row
vcard1
fn
n
L-name
L-name
ID
ID
F-name
F-name
Stefan
DeckerStefan
1
Decker
Decker
Stefan
Birgit
2
16XML
- Containment, hierarchy
- Adjacency (A followed by B)
- Attributes (atomic values)
- Opaque reference (IDREF)
- Good for serialization, poor for modeling
relational semantics
17Encoding of Information
The Creator of the Resource http//www.w3.org/Ho
me/Lassila is Ora Lassila
http//www.w3.org/Home/Lassila
Creator
Endless encoding possibilities in XML
18Introduction to RDF
- RDF (Resource Description Framework)
- Beyond Machine readable to Machine understandable
- RDF unites a wide variety of stakeholders
- Digital librarians, content-raters, privacy
advocates, B2B industries, AI... - Significant (but less than XML) industrial
momentum, lead by W3C - RDF consists of two parts
- RDF Model (a set of triples)
- RDF Syntax (different XML serialization syntaxes)
- RDF Schema for definition of Vocabularies (simple
Ontologies) for RDF (and in RDF)
19A Simple Example
- Describing Resources
- URIs global OIDs, literals
- Binary relationships between objects
- Arcs (relationships) are first-class objects
- Blank (anonymous) nodes
- Ora Lassila is the creator of the resource
http//www.w3.org/Home/Lassila - Structure
- Resource (subject) http//www.w3.org/Home/Las
sila - Property (predicate) http//www.schema.org/Cre
ator - Value (object) "Ora Lassila
sCreator
http//www.w3.org/Home/Lassila
20RDF
- Graph-based universal syntax
(Agent-) Applications
RDF-Layer (Single dataformat, Query and storage
System)
Scheduling Service
Insurance Ratings
Calendar
Semantics in a global, open environment?
21Large scale Interoperation
Source
Destination
Likely to be implemented at the vocabulary
level!!!
NormalFault is_a Data consists of
----
Re-engineering
Translation Step
Abstraction
Adaptation
ltxsdschema xmlnsxsd"http//..."gt
ltxsdannotationgt A-Schema lt/xsd...lt/xsdsche
magt
DTD or XML Schema
Conceptual Domain Model(Objects and Relations)
22Ontologies as KR Semantic Nets
- Semantic Web (and the Web) originally conceived
as a (global, decentralized, etc.) Semantic Net - Links determine meaning
- Link traversal significant
- Natural fit with hypermedia
- Two design traditions
- The Web Scruffies
- "Anything can say anything about anything"
- Principle of partial understanding
- Little semantics goes a long way
- Uniformity of representation and notation
- The Neats
- Coming out of the description logic tradition
- Decidability and practicality of key reasoning
components key - Formal semantics
- Economy and readability of notation
23Step2 Ontologies
- What is an Ontology?
- An ontology is a specification of a
conceptualization. - Tom Gruber, 1993
- Ontologies are social contracts
- Agreed, explicit semantics
- Understandable to outsiders
- (Often) derived in a community process
24Dynamic Communication Partners Interpretation
steps are too costly
25Large scale Interoperation
Source
Destination
Likely to be implemented at the vocabulary
level!!!
NormalFault is_a Data consists of
----
Re-engineering
Translation Step
Abstraction
Adaptation
ltxsdschema xmlnsxsd"http//..."gt
ltxsdannotationgt A-Schema lt/xsd...lt/xsdsche
magt
DTD or XML Schema
Conceptual Domain Model(Objects and Relations)
26OWL - Web Ontology Language
- OWL provides an RDF/XML vocabulary for defining
classes, their properties and their relationships
among classes. - Based on Description Logics
- Enables to Classes and Properties
- OWL a W3C Candidate Recommendation (see
http//www.w3.org/TR/owl-ref)
This part of the tutorial is a selection and
slight adaptation from an OWL tutorial from Roger
L. Costello and David B. Jacobs The MITRE
Corporation
27Origins of OWL
DAML
OIL
RDF
All were influenced by RDF
DAML DARPA Agent Markup Language OIL Ontology
Inference Layer
DAMLOIL
OWL
28OWL Full, OWL DL, and OWL Lite
- Not everyone will need all of the capabilities
that OWL provides. Thus, there are three
versions of OWL
OWL Full
OWL DL
OWL Lite
DL Description Logic
29OWL Primitives for Defining Properties
Symmetric if P(x,y) then P(y,x) inverseOf if
P1(x,y) then P2(y,x) Transitive if P(x,y) and
P(y,z) then P(x,z) Functional if P(x,y) and
P(x,z) then yz InverseFunctional if P(x,y) and
P(z,y) then xz allValuesFrom P(x,y) has
yallValuesFrom(C) someValuesFrom P(x,y) has
ysomeValuesFrom(C) hasValue P(x,y) and
yhasValue(I) cardinality cardinality(P)
n minCardinality minCardinality(P)
n maxCardinality maxCardinality(P)
n equivalentProperty P1 P2
30OWL Primitives for Defining Classes
intersectionOf C intersectionOf(C1, C2,
) unionOf C unionOf(C1, C2, ) complementOf
C complementOf(C1) oneOf C oneOf(I1, I2,
) equivalentClass C1 C2 disjointWith C1 !
C2 sameIndividualAs I1 I2 differentFrom I1 !
I2 AllDifferent I1 ! I2, I1 ! I3, I2 ! I3,
Thing C1, C2, , I1, I2, , P1, P2,
31OWL ObjectProperty vs. DatatypeProperty
An ObjectProperty relates one Resource to another
Resource
ObjectProperty
Resource
Resource
A DatatypeProperty relates a Resource to a
Literal or an XML Schema datatype
DatatypeProperty
Resource
Value
32Constructing Classes using Set Operators
- OWL gives you the ability to construct classes
using these set operators - intersectionOf
- unionOf
- complementOf
33Defining a class using the intersectionOf
operator
Person
femalePerson
malePerson
Child
A father is a male Person with a least one Child.
Thus, a father may be defined as the
intersectionOf the malePerson class and an
anonymous class containing the hasChild property
with At least one value from Child..
34Understanding intersectionOf
lt?xml version"1.0"?gt ltrdfRDF xmlnsrdf"http//w
ww.w3.org/1999/02/22-rdf-syntax-ns"
xmlnsrdfs"http//www.w3.org/2000/01/rdf-schem
a" xmlnsowl"http//www.w3.org/2
002/07/owl" xmlbase"http//www.
geodesy.org/water/naturally-occurring"gt
ltowlClass rdfIDFather"gt
ltowlintersectionOf rdfparseType"Collection"gt
ltowlClass rdfabout"malePerson"/gt
ltowlRestrictiongt
ltowlonProperty rdfresource"hasChildre
n"/gt ltowlallValuesFrom
rdfresource"Child"/gt ltowlmincardinality
rdfdatatype"http//www.w3.org/2001/XMLSchemanon
NegativeInteger"gt1
lt/owlmincardinalitygt
lt/owlRestrictiongt lt/owlintersectionOfgt
lt/owlClassgt ... lt/rdfRDFgt
This is read as A Father is the intersection of
the malePerson and an anonymous class that
contains a property hasChild and all values are
instances of Child. There is at least
one child." Here's an easier way to read this
A father is a male Person with at least one
child."
35The cardinality is not mandating the number of
occurrences of a property in an instance document!
- Differentiate between
- 1. In an instance document there must be at least
one child property for a father. - 2. A father has at least one child.
- Difference
- 1. The first statement is an Integrity Constraint
- 2. The second statement is an assertion. It
places no restrictions on the number of
occurrences of the hasChildren property in an
instance document. In fact, any Father resource
may zero hasChildren properties. There must be
one, however. - Data validation under Description Logic Semantics
not possible.
36Assertions vs. ConstraintsExample Cardinality
Constraints
ltowlClass rdfIDFather"gt ltrdfssubClassOf
rdfresource"malePerson"/gt
ltrdfssubClassOfgt ltowlRestrictiongt
ltowlonProperty rdfresource"hasChil
dren"/gt ltowlmincardinality
rdfdatatype"http//www
.w3.org/2001/XMLSchemanonNegativeInteger"gt1lt/owl
mincardinalitygt lt/owlRestrictiongt
lt/rdfssubClassOfgt lt/owlClassgt
This is read as "The Father class is a
subClassOf malePerson, and a subClassOf an
anonymous class which has a property hasChildren.
There must at least one child for a father.
This is indicated by a cardinality of 1." Here's
an easier way to read this "The Father class is
a subClassOf malePerson. It has a property
hasChildren. There must be at least only one
child for a father."
37Comparison
OWL Full OWL DL OWL Lite
All listed primitives. Further, you can mix RDF
Schema definitions with OWL definitions.
You cannot use owlcardinality with
TransitiveProperty. A DL ontology cannot
import an OWL Full ontology. You cannot use a
class as a member of another class, i.e., you
cannot have metaclasses. FunctionalProperty
and InverseFunctionalProperty cannot be used with
datatypes (they can only be used with
ObjectProperty).
All the DL restrictions plus You cannot use
owlminCardinality or owlmaxCardinality. The
only allowed values for owlcardinality is 0 and
1. Cannot use owlhasValue. Cannot use
owldisjointWith. Cannot use owloneOf. Cannot
use owlcomplementOf. Cannot use owlunionOf.
38Advantages/Disadvantages
- Full
- The advantage of the Full version of OWL is that
you get the full power of the OWL language. - The disadvantage of the Full version of OWL is
that it is difficult to build a Full tool. Also,
the user of a Full-compliant tool may not get a
quick and complete answer. - DL/Lite
- The advantage of the DL or Lite version of OWL is
that tools can be built more quickly and easily,
and users can expect responses from such tools to
come quicker and be more complete. - The disadvantage of the DL or Lite version of OWL
is that you don't have access to the full power
of the language.
39Ontology and Schema Languages A comparison
40Ontology Editors Protégé-2000
ltrdfsClass rdfabout"mvMotorVehicle"gt
ltrdfssubClassOf rdfresource"rdfsResource"/gtlt
/rdfsClassgt ltrdfsClass rdfabout"mvPassengerV
ehicle"gt ltrdfssubClassOf rdfresource"mvMo
torVehicle"/gtlt/rdfsClassgt ltrdfProperty
rdfabout"mvrearSeatLegRoom"
amaxCardinality"1" arange"integer"gt
ltrdfsdomain rdfresource"mvMotorVehicle"/gt
ltrdfsrange rdfresource"rdfsLiteral"/gtlt/rdf
Propertygt
41Selected Ontology Tools and Dimensions
42Further Topics
- Ontology creation and learning
- Tools and Techniques for bootstrapping
- Tools and Techniques for enhancement of existing
resources - Mappings across ontologies and schemas
- Model Management and Ontology Algebras
43The Layer Cake
Research Phase
Standardization Phase
Recommendation Phase
- Tim Berners-Lee
- Axioms, Architecture and Aspirations
- W3C all-working group plenary Meeting
- 28 February 2001
44Rules
45TRIPLE An RDF Query, Inference,and
Transformation Language
46Motivation Why Rule Languages for the Web
- Declarative Processing
- Time to Market Faster to write rules than code
for transformation, integration - Rule capture part of the dynamic aspects of a
domain - Ontologies capture static aspects
47Guiding Requirements for an RDF Rule Language
- Support RDF (graph-structured data)
- Handle multiple modeling semantics
- (OWL, DAMLOIL, UML, ER, TopicMaps, DAMLOIL,
XML-Schema, Relational Data, special purpose data
models) - Special query systems for all of them?
- Distributed, heterogeneous sources
- Not all data is created equal
48Basic Notion RDF Models
Stefans Data
Franks Data
49Implicit, Parameterized Models
50TRIPLELanguage Overview
- Native support
- for Resources namespaces,
- Abbreviations
- Models (sets of RDF statements)
- Reification
- Rules with expressive bodies (full FOL syntax)
- Inspired by F-Logic
- subjectpredicate?object (molecule)
- Extended by Models, model expressions,
parameterized models - sp?o_at_m triple lts,p,ogt in model m
- sp?o_at_(m1 ? m2) model intersection, union, diff.
- sp?o_at_sf(m1, X, Y) Skolem function
51Example Dublin Core
- dc http//purl.org/dc/elements/1.0/.
- isi http//www.isi.edu/.
-
- _at_isidocuments
- isid_01_01
- dctitle ? TRIPLE
- dccreator ? Stefan Decker
- dcsubject ? RDF
- dcsubject ? triples ... .
namespace abbreviations
model block
fact
52Parameterized Models
- General format?P1, , Pn _at_model(P1, , Pn)
clausesP1, , Pn - Used for
- Data integration
- Model transformation
- Defining the semantics of languages layered on
top of RDF (semantic spaces) - Module system
53Semantic Spaces Specifying Semantics via
Parameterized Models
- RDF Schema, UML (and other frame/OO
systems)semantics can be directly defined in
TRIPLE as a parameterized model - OIL, DAMLOIL, OWL (i.e., expressive ontology
languages, DL)requires interaction with foreign
reasoning components (e.g., DL classifier)
model
sem(model)
? Mdl _at_sem(Mdl) clauses
rules describing the semantics of a data model
54Specification of RDF Schema Semantics
namespace abbreviations
- rdf 'http//www.w3.org/...rdf-syntax-ns'.
- rdfs 'http//www.w3.org/.../PR-rdf-schema-...'
. - type rdftype.
- subPropertyOf rdfssubPropertyOf.
- subClassOf rdfssubClassOf.
- FORALL Mdl _at_rdfschema(Mdl)
- FORALL O,P,V OP-gtV lt-
- OP-gtV_at_Mdl.
-
- FORALL O,V OsubClassOf-gtV lt-
- EXISTS W (OsubClassOf-gtW
- AND WsubClassOf-gtV).
-
resource abbreviations
model block
copy triples from Mdl
Transitivity of subClassOf
55Example Cars Ontology
- _at_cars
- xyzMotorVehiclerdfssubClassOf -gt
rdfsResource. - xyzPassengerVehiclerdfssubClassOf -gt
xyzMotorVehicle. - xyzTruckrdfssubClassOf -gt
xyzMotorVehicle. - xyzVanrdfssubClassOf -gt xyzMotorVehicle.
- xyzMiniVan
- rdfssubClassOf -gt xyzVan
- rdfssubClassOf -gt xyzPassengerVehicle.
-
xyzMotorVehicle
xyzTruck
xyzVan
xyzPassengerVehicle
xyzMiniVan
X xyzVan X xyzTruck X
xyzPassengerVehicle
FORALL X lt- XrdfssubClassOf -gt
xyzMotorVehicle_at_cars.
X xyzVan X xyzTruck X
xyzPassengerVehicle X xyzMiniVan
FORALL X lt- XrdfssubClassOf -gt
xyzMotorVehicle_at_rdfschema(cars).
56Specification of UML Semantics
rdf 'http//www.w3.org/...rdf-syntax-ns'. uml
'http//www.omg.org/uml/1.3/'. FORALL Mdl
_at_uml(Mdl) FORALL O,P,V OP-gtV lt-
OP-gtV_at_Mdl. FORALL X,Z g(X,Z)rdftype-gtuml
Generalization
uml'Generalization.child'-gtX
uml'Generalization.parent'-gtZlt- EXISTS
Y,G1,G2 G1uml'Generalization.child'-gtX
uml'Generalization.parent'-gtY AND
G2uml'Generalization.child'-gtY
uml'Generalization.parent'-gtZ .
Transitivity of Generalization
57DAMLOIL Semantics
- daml_oil(Mdl) model realized by accessing a DL
classifier (e.g., Racer or FaCT) - access only allowed in rule bodies
- realization Mdl is materialized and transformed
into input for DL classifier classifier is
invoked (direct) subClassOf and sameClassAs
added to daml_oil(Mdl) model rest handled via
TRIPLE rules directly
rules for remaining semantics
ontology
daml_oil(ontology)
subClassOfsameClassAs
DL classifier
mat.
?O _at_daml_oil(O) clauses
- results in hybrid rule language similar to Carin,
but more pragmatic approach powerful but
incomplete
58DAMLOIL Example
- daml 'http//www.daml.org/.../damloil'.
- animals 'http//www.example.org/animals'.
- _at_animalsontology
- animalsAnimalrdftype -gt damlClass.
- animalsHerbivorerdftype -gt damlClass
- damlsubClassOf -gt animalsAnimal.
- animalsCarnivorerdftype -gt damlClass
- damlsubClassOf -gt animalsAnimal
- damldisjointWith -gt animalsHerbivore.
- animalsOmnivorerdftype -gt damlClass
- damlsubClassOf -gt animalsHerbivore
- damlsubClassOf -gt animalsCarnivore.
-
- FORALL C
- lt- CdamlsameClassAs -gt damlNothing_at_daml_oil(
animalsontology).
Animal
s
s
Herbivore
Carnivore
damldisjointWith
s
s
Omnivore
s damlsubClassOf
find all unsatisfiable classes(will detect
Omnivore)
59Realization Mapping to Horn Logic
- First implementation by mapping to Horn Logic /
XSB system (Prolog with tabled resolution) - model theory for full logic completed
- Lloyd-Topor transformation for quantifiers etc.
- RDF-specific transformations given as rewrite
rules
60Realization Compilation to Horn logic
- First implementation (and informal semantics) by
mapping to Horn Logic / XSB system (Prolog with
tabled resolution) - Lloyd-Topor transformation for quantifiers etc.
- RDF-specific transformations given as rewrite
rules
61triple.semanticweb.org
62Web Services
63Web Services vs. the (Semantic) Web
- Semantic Web To do for KR what the Web did for
hypertext - Web services To exploit Web infrastructure for
distributed applications - The problem of Discovery
- Suppose the set of WSs grows like the Web
- Difficulties
- Finding compatible services
- Finding services with desired functionality
- Finding services with desired other properties
(cost, QOS, location) - Controlling effort put into these searches
- All this is much more difficult for compositions
(or possible compositions)
Slides from Bian Parsia
64UDDI and the Problem of Discovery
- UDDI is "a 'meta service' for locating web
services by enabling robust queries against rich
metadata." --UDDI 3.0 Specification - UDDI is a Web service
- It has an API (on the Web).
- Easy to conceptualize as an application
- UDDI has a "rich" metadata model
- Developed specifically for UDDI
- "Until now, there has been no central way to
easily get information about what standards
different services support and no single point of
access to all markets of opportunity, allowing
them to easily connect with all potential service
consumers." (pg. 1) - Until now, the Web has shown that central ways,
single points of access, aiming for all
markets just dont work at Web scale - Good bet From now, the Web will continue to show
this - The UDDI players (e.g., Microsoft) tried this
before and lost
Slides from Bian Parsia
65Fixing UDDI
- Evolutionary (Focus on simple Discovery)
- Why should WSs reinvent the wheel?
- Treat Semantic Web tech and standards like
current Web tech and standards - Natural progression
- First keywords, then tModels
- Then "taxonomies
- Then incorporating Semantic Web ontologies
- Mapping DAML-S profiles into tModels
- Then moving from tModels to Ontologies
- Radical
- Decentralize, decentralize, decentralize
- Design representations for automatable reasoning
- And be expansive about what sorts of reasoning
you desire - Small gains add up!
- Doing it right, or well, or both, is worth it
Slides from Bian Parsia
66Ontology-based Service Discovery in P2P-Networks
67Semantic Routing (for Data, Queries)
- Route information based on content and ontologies
instead of IP-addresses - Applications
- Discovery of distributed Web Services in
Peer-to-Peer Networks - Motivation Avoid centralized database (UDDI -
single point of failure, man in the middle,
freshness) - Publish/Subscriber Models
- Metadata-based File Exchange in P2P networks
68P2P Infrastructure for Semantic Web Services
- (Semantic) Web Services
- Large network of service providers capable of
instantiating high-level task descriptions in
distributed fashion - How to reach all service providers that are
potentially interesting?
You Are Here
Service Providers
69Idea
- Define a multi-dimensional Overlay Network (on
top of the IP-network) for a P2P network - Map Ontology-terms to network dimensions
- Each dimension identifies a term
- Route information to dimension
- Cayley-graphs provide a good starting point
- Tricky part
- Keep network organized when nodes join and leave
the network - Define Mapping
70Cayley Graphs
- Graph representing a permutation group G,
described by a set of generators Akers,
Krishnamurty - Regular, vertex-symmetric, recursively
decomposable - Optimal routing and broadcast algorithms exist
111
000
Hypercube
Star Graph
71Hypercube Topology
- Broadcast algorithm
- Tag message with dimension of link on which it is
sent and forward message only on links of higher
dimension - Properties
- Network diameter, characteristic path length,
node degree are of O(logbN) - Fault tolerance, vertex symmetry
Step 2
Step 3
Step 1
72Topology Construction Algorithm Sketch
- Topology of next biggest complete hypercube is
always implicitly present in any current network
topology - Allows for hypercube algorithms (broadcast,
search) to run - Node departures Neighbors of a departing node
jump in to cover the position(s) previously
occupied and covered by the departing node - Complete hypercube topology is collapsed and
stored among the existing nodes, allowing for any
number of nodes in the network - Node arrivals Collapsed topology is
reconstructed, new node takes over responsibility
for one or more positions - Unfold topology by retrieving topology
information from nodes in the network
73Topology Construction I
II
I
74Topology Construction II
IV
III
75Topology Construction III
VI
V
76Topology Construction IV
VI
V
3-Hypercube
77Ontology based Routing
- Goal Use additional global knowledge to improve
search performance of P2P network - Contain broadcast of search messages to
potentially interesting peers - Approach Partitioning of network into concept
clusters - Clusters are assigned to concepts organized in an
ontology
Service Ontology
Domain Ontology
78HyperCuP Network Construction I
- Use concepts A, D, E, F as structuring concepts
C0..C3
79HyperCuP Network Construction II
- Create concept cluster Cayley graph by grouping
similar peers
Peer address Storage coordinates 3214
80HyperCuP Network Construction III
- Link concept clusters by outer hypercube
topology
ØDelivery Ù ØRetail Ù Wholesale Ù Motor Vehicles
Delivery Ù ØRetail Ù ØWholesale Ù Motor Vehicles
Peer address Storage coordinates 3214
Concept coordinates 1001
81Querying the HyperCuP Network I
- Queries Logical conjunctions and disjunctions of
negated and non-negated ontology concepts - Example Wholesale Ú (Delivery Ù Retail) C2 Ú
(C0 Ù C1) - Logical minimization of query
- To retrieve logical minterms in query
- Minterm locality
- Minterms represent larger clusters of concept
clusters
C0C1C2C3
82Querying the HyperCuP Network II
- Routing to concept clusters
- Broadcast search message among concept clusters
as determined by minterms - HyperCuP Broadcast in concept clusters
- Inform all peers inside all addressed clusters
1.
4.
4.
3.
0.
3.
2.
4.
4.
Wholesale Ú (Delivery Ù Retail) C2 Ú (C0 Ù C1)
3.
83p2p.semanticweb.org
84Outlook From the Semantic Web to the Semantic
Desktop
- Who is satisfied with his/her current Computer
Desktop? - Co-evolution enabled between the Semantic Web and
the Semantic Desktop - Metadata creation and Application integration
85Conclusion
- The Semantic Web is forced by various
developments (e-Commerce, e-Science, e-Society) - Addition of Semantics add new perspective on
Rules, Distributed Computing, many other things
in Computer Science
86Conclusion II
- The best way to predict the future is to invent
it - (be part of it you make a difference!)
87Collaborators
- This presentation would not have been possible
without the help, hard work, enthusiasm, and
slides of many people I was lucky enough to work
with - Sergey Melnik
- Michael Sintek
- Mario Schlosser
- Martin Lacher
- Wolfgang Nejdl
- Yuhui Jin
- Prasenjit Mitra
- Gio Wiederhold
- Bijan Pasia