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Relationships at the Heart of Semantic Web
Amit Sheth
Large Scale Distributed Information Systems (LSDIS) LabUniversity Of Georgia; http://lsdis.cs.uga.edu
CTO, Semagix, Inc. http://www.semagix.com
November 2002© Amit Sheth
Keynote
SOFSEM 2002 , Milovy, Czech Republic, Nov 25 2002
The Semantic Web -- a vision with several views:•·“The Web of data (and connections) with meaning in the sense that a computer program can learn enough about what data means to process it.” [B99]•·“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.” [BHL01]•·“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications. [W3C01]
Semantics: The Next Step in the Web’s Evolution
Semantics for the Web
On the Semantic Web every resource (people, enterprises, information services, application services, and devices) are augmented with machine processable descriptions to support the finding, reasoning about (e.g., which service is best), and using (e.g., executing or manipulating) the resource. The idea is that self-descriptions of data and other techniques would allow context-understanding programs to selectively find what users want, or for programs to work on behalf of humans and organizations to make them more efficient and productive.
Move from Syntax to Semantics in Information System (a personal perspective)
Semantic Web, some DL-II projects,Semantic Web, some DL-II projects,Semagix SCORE, Applied SemanticsSemagix SCORE, Applied Semantics
VideoAnywhereVideoAnywhereInfoQuiltInfoQuilt
OBSERVEROBSERVER
Generation IIIGeneration III(information
brokering)
1997...1997...
Semantics (Ontology, Context, Relationships, KB)
InfoSleuth, KMed, DL-I projectsInfoSleuth, KMed, DL-I projectsInfoscopes, HERMES, SIMS, Infoscopes, HERMES, SIMS,
Garlic,TSIMMIS,Harvest, RUFUS,...Garlic,TSIMMIS,Harvest, RUFUS,...
Generation IIGeneration II(mediators)
1990s1990s
VisualHarnessVisualHarnessInfoHarnessInfoHarness
Metadata (Domain model)
MermaidMermaidDDTSDDTS
Multibase, MRDSM, ADDS, Multibase, MRDSM, ADDS, IISS, Omnibase, ...IISS, Omnibase, ...
Generation IGeneration I(federated DB/
multidatabases)
1980s1980s
Data (Schema, “semantic data modeling)
Semantics and Relationships
Semantics is derived from relationships. Consider the linguistics perspective.
“Semantics is the study of meaning. …We may distinguish a number of legitimate ways to approach semantics:
• …• the relationship between linguistic
expressions (e.g. synonymy, antonymy, hyperonymy, etc.): sense;
• the relationship to linguistic expressions to the "real world": reference. “
Ontologies in KR help capture the above.Quoted part from http://www.ncl.ac.uk/sml/staff/. © 2000 Jonathan West.
Why is this a hard problem?
Are objects/entities equivalent/equal(same)?How (well) are they related? • partial and fuzzy match: related, relevant• related in a “context”• degrees: semantic similarity, semantic proximity,
semantic distance, ….– [differentiation, disjointedness]
• Even is-a link involves different notions: identify, unity, essense (Guarino and Wetley 2002)
Semantic ambiguity, also based on incomplete, inconsistent, approximate information/knowledge
Many problems have stumbled across these issues e.g., schema integration (in database management area)
Semantics and Relationships
Increasing depth and sophistication in dealing with semantics by dealing with (identifying/searching to analyzing) documents, entities, and relationships.
Documents
Entities
RelationshipsFuture
Current
Past
Issues - Relationships
• Identifying Relationship (extraction)• Expressing (specifying, representing)
relationships • Discovering and Exploring
Relationships• Hypothesizing and Validating
Relationships• Utilizing/exploiting Relationships for
Semantic Applications (in document search, querying metadata, analysis…)
Expressing Relationships
• Expressiveness of specification language– In relational model– In semantic data model, e.g., E-R
variants– KR languages – In logic, e.g., description logics– …
Relationship Modeling in Various Representation Models …
Catalog/ID
GeneralLogical
constraints
Terms/glossary
Thesauri“narrower
term”relation
Formalis-a
Frames(properties)
Informalis-a
Formalinstance
Value Restriction
Disjointness, Inverse,part of…
After Deborah L. McGuinness (Stanford) and Tim Finin (UMBC)After Deborah L. McGuinness (Stanford) and Tim Finin (UMBC)
SimpleTaxonomies
ExpressiveOntologies
Wordnet
CYCRDF DAML
OO
DB Schema RDFS
IEEE SUOOWL
UMLS
Sampling Issues in Relationships-outline of this talk• Simple Relationships – already known
– Representation– Identification/Querying: “Which entities are related
to entity X via relationship R?” where R is typically specified as possibly a join condition or path expression
• Complex relationships– Rho: discovery from large document set with
associated metadata and ontologies: “How is X related to Y?”
– ISCAPEs: validation/ human-directed knowledge discovery
Metadata and Ontology: Primary Semantic Web enablers
Data (Heterogeneous Types/Media)(Heterogeneous Types/Media)
Content Independent Metadata (creation-date, location, type-of-sensor...)(creation-date, location, type-of-sensor...)
Content Dependent Metadata (size, max colors, rows, columns...)(size, max colors, rows, columns...)
Direct Content Based Metadata (inverted lists, document vectors, LSI)(inverted lists, document vectors, LSI)
Domain Independent (structural) Metadata (C++ class-subclass relationships, HTML/SGML(C++ class-subclass relationships, HTML/SGML Document Type Definitions, C program structure...)Document Type Definitions, C program structure...)
Domain Specific Metadata area, population (Census),area, population (Census), land-cover, relief (GIS),metadata land-cover, relief (GIS),metadata concept descriptions from ontologiesconcept descriptions from ontologies
OntologiesClassificationsClassificationsDomain ModelsDomain Models
User
More
Seman
tics
for
Releva
nce
to ta
ckle
Info
rmatio
n
Overlo
ad!!
Metadata adapter
Metadata adapter
Enterprise Content Applications
SCORE technology
KnowledgeAgent
Monitor
KS
KS
KS
KS
KA
KA
KA
KnowledgeSources
KnowledgeAgents
KAToolkit
Ontology
Metabase
Sem
i-St
ruct
u red
ContentSourcesContentSources
CA
CA
CA
ContentAgent
Monitor
ContentAgents
CAToolkit
Databases
XML/Feeds
Websites
Reports
Documents
Stru
ctu r
edU
nstr
uct u
red
Databases
XML/Feeds
Websites
Reports
Semantic Enhancement ServerEntity Extraction,
Enhanced Metadata,Domain Experts
AutomaticClassification
Classification Committee
Semantic Query ServerOntology
andMetabase
Main Memory Index
Information Extraction for Metadata Creation
WWW, EnterpriseRepositories
METADATAMETADATA
EXTRACTORSEXTRACTORS
Digital Maps
NexisUPIAPFeeds/
Documents
Digital Audios
Data Stores
Digital Videos
Digital Images. . .
. . . . . .
Key challenge: Create/extract as much (semantics)metadata automatically as possible
Video withEditorialized Text on the Web
AutoCategorization
AutoCategorization
Semantic MetadataSemantic Metadata
Automatic Classification & Metadata Extraction (Web page)
Semantic Annotation
Limited tagging(mostly syntactic)
COMTEX Tagging
Content‘Enhancement’Rich Semantic
Metatagging
Value-added Voquette Semantic Tagging
Value-addedrelevant metatagsadded by Voquetteto existing COMTEX tags:
• Private companies • Type of company• Industry affiliation• Sector• Exchange• Company Execs• Competitors
Automatic Semantic Annotation of Text:Entity and Relationship Extraction
Extraction Agent
Enhanced Metadata Asset
Ontology-directed Metadata Extraction (Semi-structured data)Web Page
Semantic Metadata
Syntax Metadata
Entity and Semantic Metadata Extraction
Enabling powerful linking of actionable information and facilitating important semantic applications such as knowledge discovery and link analysis
(user’s task of manually retrieving all the information he needs to know is greatly minimized; he can spend more time making effective decisions)
Semantic Metadata Content TagsCompany: Cisco Systems, Inc.Classification: Channel Partners,
E-Business SolutionsChannel Partner: Siemens NetworkChannel Partner: Voyager NetworkChannel Partner: Siemens NetworkChannel Partner: Wipro GroupE-Business Solution: CI S-1270 SecurityE-Business Solution: CI S-320 LearningE-Business Solution: CI S-6250 FinanceE-Business Solution: CI S-1005 e-MarketTicker: CSCOI ndustry: Telecommunication, . . .Sector: Computer HardwareExecutive: J ohn ChambersCompetition: Nortel Networks
Syntactic MetadataProducer: BusinessWireSource: BloombergDate: Sept. 10 2001Location: San J ose, CAURL: http:/ /bloomberg.com/1.htmMedia: Text
XML content item with enriched semantic tagging, ready to be queried
E-Business SolutionOntology
CiscoSystems
VoyagerNetwork
SiemensNetwork
WiproGroup
UlysysGroup
CIS-1270 Security
CIS-320Learning
CIS-6250 Finance
CIS-1005 e-Market
Channel Partner
belongs to
- - -
Ticker
represen
ted b
y
- - -
- - -
- - -
- - -
Industry
chan
nel p
artn
er of
- - -
- - -
- - -
- - -
Competitioncompetes with
provider of
- - -
- - -
- - -
- - -
Executives
works
for
- - -
- - -
- - -
- - -
Sectorbelo
ngs
to
Semantic Enhancement
Uniquelyexploiting
real-worldsemantic
associationsin the right
context
SemanticMetadataExtraction
(also syntactic)
Content TagsSemantic MetadataClassification: Channel Partners,
E-Business SolutionsCompany: Cisco Systems, Inc.
Syntactic MetadataProducer: BusinessWireSource: BloombergDate: Sept. 10 2001Location: San J ose, CAURL: http: //bloomberg.com/1.htmMedia: Text
ChannelPartners
E-BusinessSolutionsClassification
Content Tags
Semantic MetadataClassification: Channel Partners,
E-Business Solutions
Classification CommitteeKnowledge-base, Machine Learning &
Statistical Techniques
Semantic Metadata Enhancement
Focused relevantcontent
organizedby topic
(semantic categorization)
Automatic ContentAggregationfrom multiple
content providers and feeds
Related relevant content not
explicitly asked for (semantic
associations)
Competitive research inferred
automatically
Automatic 3rd party content
integration
Semantic Application Example – Research Dashboard
Related Stock
News
Related Stock
News
Semantic Web – Intelligent Content
IndustryNews
IndustryNews
Technology Products
Technology Products
COMPANYCOMPANY
SECEPAEPA
RegulationsRegulations
CompetitionCompetition
COMPANIES in Same or Related INDUSTRY
COMPANIES inINDUSTRY with Competing PRODUCTS
Impacting INDUSTRY or Filed By COMPANY
Important to INDUSTRY or COMPANY
Intelligent Content = What You Asked for + What you need to know!
Syntax Metadata
Semantic Metadata
led by
Same entity
Human-assisted inference
Knowledge-based andManual Associations
Blended Semantic Browsing and Querying (Intelligence Analyst Workbench)
Physical link to Relationship
<TITLE> A Scenic Sunset at Lake Tahoe </TITLE>
<p>
Lake Tahoe is a popular tourist spot and <A HREF = “http://www1.server.edu/lake_tahoe.txt”>some interesting facts</A> are available here. The scenic beauty of Lake Tahoe can be viewed in this photograph:<center><IMG SRC=“http://www2.server.edu/lake_tahoe.img”></center>
Correlation achieved by using physical linksDone manually by user publishing the HTML document
MREFMetadata Reference Link -- complementing HREF
Creating “logical web” through Media Independent Metadata
based Correlation
Metadata Reference Link (<A MREF …>)
• <A HREF=“URL”>Document Description</A>
physical link between document (components)
• <A MREF KEYWORDS=<list-of-keywords>; THRESH=<real>>Document Description</A>
• <A MREF ATTRIBUTES(<list-of-attribute-value-pairs>)>Document Description</A>
Abstraction Layers
METADATA
DATA
METADATA
DATAMREFin RDF
ONTOLOGYNAMESPACE
ONTOLOGYNAMESPACE
Model for LogicalCorrelation using Ontological Terms and Metadata
Framework for Representing MREFs
Serialization(one implementation choice)
height, width and size
water.gif (Data)Metadata Storage
water.gif
……mpeg
……ppm
Major component(RGB)Major component(RGB)
Blue
Content based MetadataContent based Metadata
ContentDependentMetadata
Correlation based on Content-based Metadata
Some interesting information on dams is available here.“information on dams” is defined by an MREF defining keywords and metadata (which may be used for a query).
An Example RDF Model for MREF
<?namespace href="http://www.foo.com/IQ" as="IQ"?> <?namespace href="http://www.w3.org/schemas/rdf-schema" as="RDF"?> <RDF:serialization> <RDF:bag id="MREF:12345>
<IQ:keyword><RDF:resource id="constraint_001">
<IQ:threshold>0.5</IQ:threshold> <RDF:PropValue>dam</RDF:PropValue>
</RDF:resource></IQ:keyword><IQ:attribute>
<RDF:resource id="constraint_002"><IQ:name>majorRGB</IQ:color><IQ:type>string</IQ:type><RDF:PropValue>blue</RDF:PropValue> </RDF:resource>
</IQ:attribute> </RDF:bag></RDF:serialization>
Domain Specific Correlation
Potential locations for a future shopping mall identified by all regions having a population greater than 500 and area greater than 50 sq meters having an urban land cover and moderate relief <A MREF ATTRIBUTES(population < 500; area < 50 & region-type = ‘block’ & land-cover = ‘urban’ & relief = ‘moderate’)>can be viewed here</A>
=> media-independent relationships between domain specific metadata: population, area, land cover, relief
=> correlation between image and structured data at a higher domain specific level as opposed to physical “link-chasing” in the WWW
TIGER/Line DB
Population: Area:
Boundaries:
Land cover:Relief:
Census DB
Map DB
Regions(SQL)
Boundaries
Image Features(IP routines)
Repositories and the Media Types
Relationship Discovery
• Problem Huge volumes of data. Need to find
relationships between two entities in the Semantic Web.
Application areas such as National Security, Intelligence Services, Bioinformatics.
Relationship can be of different kinds.
passengerOf
AlQaida
Terrorist Organization
leaderOf
friendOf
Mohammad Atta
Example
Osama, bin laden
Ramzi Binalshibh
namename
memberOf
name
Semantic Association
Complex relationships which capture connectivity and similarity of entities in a knowledge base– Complex
• Involve multiple relations
– Connectivity• Includes both directed paths and undirected
paths Similarity• Specific notion of an isomorphism, based on
the similarity of roles of entities.
Representing and analyzing metadata
• By using a graph data model, Semantic Associations can be viewed in terms of graph traversals
• We can distinguish between different types of Semantic Associations based on structural properties
• For example, a path, intersecting paths, isomorphic paths.
• We use the RDF Graph Data Model, to model Semantic Associations.
Example Graph
&r3
&r5
“Reina Sofia Museun”
&r7
“oil on canvas”
&r2
2000-02-01
“oil on canvas”
&r8“Rodin
Museum”
“image/jpeg”
2000-6-09
Ext. Resource
String
Date
Integer
String
title
file_size
last_modified
mim
e-ty
pe
Artist
Sculptor
Artifact
Sculpture
Museum
String
String
String fname
lname
creates exhibited
sculpts
StringPaintingPainterpaints technique
material
typeOf(instance)
subClassOf(isA)
subPropertyOf
mime-type
exhibited
technique
exhibited
title
last_modified
last_modified
title
technique
exhibited
“Rodin”
“August”
&r6
&r1
fname
lname
fname
lname
paints
paints
creates
&r4
“Rembrandt”
“Pablo”
“Picasso”
fname
-pathConnected(x, y): is true if there is a path
<x, p1, a, p2, b, p3, …. y> in the knowledge base
XY
ap1 p2
&r3
&r5
“Reina Sofia Museun”
&r7
“oil on canvas”
&r2
2000-02-01
“oil on canvas”
&r8“Rodin
Museum”
“image/jpeg”
2000-6-09
Ext. Resource
String
Date
Integer
String
title
file_size
last_modified
mim
e-ty
pe
Artist
Sculptor
Artifact
Sculpture
Museum
String
String
String fname
lname
creates exhibited
sculpts
StringPaintingPainterpaints technique
material
typeOf(instance)
subClassOf(isA)
subPropertyOf
mime-type
exhibited
technique
exhibited
title
last_modified
last_modified
title
technique
exhibited
“Rodin”
“August”
&r6
&r1
fname
lname
fname
lname
paints
paints
creates
&r4
“Rembrandt”
“Pablo”
“Picasso”
fname
X
k
a
-joinConnected(x, y): is true if there two paths P1, P2 such that: P1 = <x, pa, a, pb, b, pc, c, pd…k, pl l, pm, m> and P2 = <y, pu, b, pv,…k, pw, l, py, n>
OrP1 = < a, pa, b, pb,…k, pk, l, pl, x > and P2 = < y, pu, b, pv, m, pw, l,…k, p5, l, p6, n >
my b
n
Painting
&r3
&r5
“Reina Sofia Museun”
&r7
“oil on canvas”
&r2
2000-02-01
“oil on canvas”
&r8“Rodin
Museum”
“image/jpeg”
2000-6-09
Ext. Resource
String
Date
Integer
String
title
file_size
last_modified
mim
e-ty
pe
Artist
Sculptor
Artifact
Sculpture
Museum
String
String
String fname
lname
creates exhibited
sculpts
StringPainterpaints technique
material
typeOf(instance)
subClassOf(isA)
subPropertyOf
mime-type
exhibited
technique
exhibited
title
last_modified
last_modified
title
technique
exhibited
“Rodin”
“August”
&r6
&r1
fname
lname
fname
lname
paints
paints
creates
&r4
“Rembrandt”
“Pablo”
“Picasso”
fname
X
Y
pa
pa
a
u
pc
p1
c
1
-isoConnected(x, y) is true if there two paths P1, P2 such that: P1 = <x, pa, a, pb, b, pc, c> and P2 = <y, pu, b, pv, m, pw, l>
andx y, a b, c l …….pa pu, pb pv, pc pw ….
Operators
• The Operator computes Semantic Associations between two entities.
• Three kinds of Operators are defined. Path : This operator returns all paths between
two entities in the data model. Connect : This operator returns intersecting
paths, on which the two entities lie. Iso : -isomorphic paths implies a similarity of
nodes and edges along the paths, and returns such similar paths between entities.
Formalism
-pathConnected(x, y): is true if there is a path– <x, p1, a, p2, b, p3, …. y> in the knowledge base
-joinConnected(x, y): is true if there two paths P1, P2 such that:
– P1 = <x, pa, a, pb, b, pc, c, pd…k, pl l, pm, m> and
– P2 = <y, pu, b, pv,…k, pw, l, py, n>
Or
– P1 = < a, pa, b, pb,…k, pk, l, pl, x > and
– P2 = < y, pu, b, pv, m, pw, l,…k, p5, l, p6, y >
Complex Relationship Validation
• Arise in several contexts, especially involving multiple ontologies (hence mappings)– information interoperability where related
resources subscribe to different but related ontologies
– information requestor and resource modelers choose to use different ontologies
– information requests to support analysis, knowledge discovery, decision making, learning that requires linking multiple domains with different ontologies
Developing all encompassing, unified ontology is not shown to be practical. Preexisting classifications/metadata standards/taxonomies are hard to ignore.
Complex Relationships - Cause-Effects & Knowledge discovery
VOLCANO
LOCATIONASH RAIN
PYROCLASTICFLOW
ENVIRON.
LOCATION
PEOPLE
WEATHER
PLANT
BUILDING
DESTROYS
COOLS TEMP
DESTROYS
KILLS
Knowledge Discovery - Example
Earthquake Sources Nuclear Test Sources
Nuclear Test May Cause Earthquakes
Is it really true?
Complex Relationshi
p:How do you model
this?
Inter-Ontological Relationships
A nuclear test could have caused an earthquakeif the earthquake occurred some time after thenuclear test was conducted and in a nearby region.
NuclearTest Causes Earthquake
<= dateDifference( NuclearTest.eventDate,
Earthquake.eventDate ) < 30
AND distance( NuclearTest.latitude,
NuclearTest.longitude,
Earthquake,latitude,
Earthquake.longitude ) < 10000
Knowledge Discovery - Example
When was the first recorded nuclear test conducted?
Find the total number of earthquakes with a magnitude5.8 or higher on the Richter scale per year starting from 1900
1950
Increase in number of earthquakes since 1945
Knowledge Discovery – exploring relationship…
For each group of earthquakes with magnitudes in the ranges5.8-6, 6-7, 7-8, 8-9, and >9 on the Richter scale per yearstarting from 1900, find number of earthquakes
Number of earthquakes with magnitude > 7 almost constant. So nuclear tests probably only cause earthquakes with magnitude < 7
Knowledge Discovery - Example…
Find nuclear tests and earthquakes that may have occurred as a result of the test
KB
InfoQuilt System Core capabilities
• Ability to handle heterogeneous, static or dynamic content – wrappers & extractors, with resource modeling (completeness, data characteristics, binding patterns)
• Information Extraction: Semi-Automatically or Automatically create domain-specific or contextually relevant metadata
• Domain modeling with complex (user defined, inter-ontology) relationships, domain rules and FD
• User defined Functions (esp. for fuzzy/approximate matching) and Simulation
• Post processing result analysis (e.g., chart creator)
IScape (Information Scape)
A computing paradigm that allows users to query and analyze the data available from a diverse autonomous sources, gain better understanding of the domains and their interactions as well as discover and study relationships.
IScape …a simple example
• user’s request– for semantically related information
(regardless of all forms of heterogeneity)– specified in terms of components of knowledge base
(domain model, relationships, functions, simulations)
“Find all earthquakes with epicenter less than 5000 mile from the location at latitude 60.790 North and longitude 97.570 East and find all tsunamis that they might have caused”
Next - KD using ISacpes
Ontologies
Disaster
eventDate
description
site => latitude, longitude
sitelatitude
longitude
Natural Disaster
Man-made Disaster
damage
numberOfDeaths
damagePhoto
Volcano
EarthquakeNuclearTest
magnitude
bodyWaveMagnitude
conductedBy
explosiveYield
bodyWaveMagnitude < 10
bodyWaveMagnitude > 0
magnitude < 10
magnitude > 0
Terms/Concepts(Attributes)
Functional Dependencies
(FDs)
Domain Rules
Hierarchies
Knowledge Builder
IScape Builder
IScape Execution
IScape
Plan Plan
Knowledge
IScape
Query
Query
QueryData retrieved
Final Results
Final Results
IScape 1
NuclearTestsDB( testSite, explosiveYield, waveMagnitude, testType, eventDate, conductedBy, [dc] waveMagnitude > 3 );
NuclearTestSites( testSite, latitude, longitude );
SignificantEarthquakesDB( eventDate, description, region, magnitude, latitude, longitude, numberOfDeaths, damagePhoto, [dc] eventDate > “January 1, 1970” );
NuclearTest( testSite, explosiveYield, waveMagnitude, testType, eventDate, conductedBy, latitude, longitude, waveMagnitude > 0, waveMagnitude < 10, testSite -> latitude longitude );
Earthquake( eventDate, description, region, magnitude, latitude, longitude, numberOfDeaths, damagePhoto, magnitude > 0 );
“Find all nuclear tests conducted by India or Pakistan after January 1, 1995 with seismic body wave magnitude > 4.5 and find all earthquakes that could have been
caused due to these tests.”
NuclearTest Causes Earthquake <= dateDifference( NuclearTest.eventDate, Earthquake.eventDate ) < 30 AND distance( NuclearTest.latitude, NuclearTest.longitude, Earthquake,latitude, Earthquake.longitude ) < 10000
Ontology Ontology
ResourceResource
Resource
Relationship
IScape
USGS sitehttp://sun00781.dn.net/nuke/hew/Library/Catalog
IScape Processing Monitor
Future
Future work in Semantic Web will increasingly focus on all dimensions of relationships, especially complex relationships.
New Semantic Applications (business/govt. intelligence) are being enabled.
Further Information• Related Paper: Sheth, Arpinar, Kashyap:
Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationshipshttp://lsdis.cs.uga.edu/lib/2002.html
• InfoQuilt and Semantic Association Projects at the LSDIS Lab: http://lsdis.cs.uga.edu
• Green, Bean and Myaeng: The Semantics of Relationships: An Interdisciplinary Perspective, Kluwer Academic Publishers 2002.