On Community Web Portals and the Semantic Web: A Database Perspective

1
On Community Web Portals and the Semantic Web
A Database Perspective
Vassilis Christophides Computer Science
Department, University of CreteInstitute for
Computer Science - FORTHHeraklion, Crete
2
Portalmania!
3
Portals Classification
Existing Communities
On-line Communities
4
Elements of Comparison
Horizontal Portals Vertical or Thematic Portals E-marketplaces
Scope Internet-oriented Subject-oriented Industry-oriented
Mission reference points for the general user promote access to information promote economic activity
Methods voluntary registration human or robot-driven resource collection expert selection of resources voluntary participation by companies

• Gateways to WWW resources with the aim of making
  information/service research simpler and more
  effective

5
Portals Classification
6
Common Objectives/Goals

• Community Knowledge Management
• Ranging from simple vocabularies to formal
  ontologies
• Aggregation/Integration of Community Content
• Ranging from unstructured (documents) to
  semi-structured (web sites) and structured
  information (data)
• Collaboration Messaging
• Ranging from simple to advanced task management
  (synchronous/ asynchronous)
• System Integration Security
• Front end to application servers/ workflow
  systems
• User Personalization (pull) Syndicated
  Content Subscription (push)
• role-based access control
• information filtering (contexts/viewpoints)
• customizable information rendering
• location/time specific information

7
Community Web Portals Knowledge Management
8
Knowledge Processes in Corporate Communities
Generating new knowledge
Accessing knowledge from external sources
Representing knowledge in documents and databases
Embedding knowledge in services and processes
Dissemination of knowledge within organisation
Using knowledge in decision making
9
Knowledge Practices in Corporate Communities
10
Corporate Communities Web Portals
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Community Web Portals Resource Descriptions
Collection Static Dynamic
Individual
Metadata Type Resources Nature
Automatic Query Mediation
Manual
Content Descriptive (unstructured,
semistructured, structured)

Semantics
Manual Manual
Manual
Structure Descriptive (semistructured,
structured)

Syntax
Automatic Automatic
Automatic
Management (all kinds)
System
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Community Web Portals A Broader Functional View
Presentation Services
Multiple Style Sheets Virtual Documents
Access and Integration Services
Content Syndication
Task Management
System Management
Classification Metadata
Security Network
Application Integration
Information Services
Personalization Services
Collaboration Services
Description, Search Docs Repositories
Messaging Workflow
Annotations, Recommendations
13
Corporate Communities Web Portals
14
Development Process of a Community Web Portal
Evaluation
15
Some Portals Market Facts
16
Some Portals Market Facts
17
On the Semantic Web

• Main infrastructure for supporting Community Webs
• groups of people sharing a domain of discourse
  and a set of information resources (e.g., data,
  documents, services) and having some common
  interests/objectives
• Higher Quality Web Information Services
• having data and programs described in a way that
  facilitates their reuse and integration by
  machines across applications

Workplace
Education
Semantic Web
Commerce
Health
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Metadata exists for Almost Anything/Everywhere

• Physical Objects, Places,
• People,
• Devices, Networks,
• Infrastructure,
• Digital Documents, Data,
• Programs,
• User Profiles, Preferences,

lttag1gt lttag2gt lttag3gt lt/tag1gt
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RDF Objectives

• Enables communities to define their own semantics
  of resource descriptions
• we can disagree about semantics, but share the
  same infrastructure (syntax, editors, query
  languages, databases, etc.)
• Imposes structural constraints on the expression
  of metadata in various application contexts
• for consistent encoding, exchange and processing
  of metadata on the Web
• Facilitates development of metadata vocabularies
  without central coordination
• mechanisms for reusing descriptions of resources,
  concepts, etc.
• Focus on DBMS technology for RDF metadata
• Related W3C efforts on XML data management

20
Looking at existing RDF Applications

• Publishing/News
• Biblink
• Scholarly Link Specification (Slinks)
• Rich Site Summary (RSS)
• Education/ Academic
• Common European Research Information Format
  (CERIF)
• Mathematics International
• Universal
• IMS Global Learning Consortium
• Cultural Heritage/ Archives/ Libraries
• Inter. Committee for Documentation Reference
  Schema (CIDOC)
• Research Support Libraries Colle ction Level
  Description (RSLP-CLD)
• EUropean Libraries Electronic Resources in
  Mathematical Sciences (Euler)

• Audio-visual
• Internet Movie DataBase (IMDB)
• Ubiquitous/Mobile/Grid Computing
• Composite Capability/Preference Profile (CC/PP)
• RDF Calendar Task Force
• Scheduler Allocation Ontology(SAO)
• E-commerce
• Basic Semantic Registry (BSR)
• Real Estate Data Consortium
• Universal Standard Products and Services
  Classification (UNSPSC)
• Geospatial/ Environmental
• Geography Markup Language(GML)
• Costal Zone Management Ontology
• Biology/Medecine
• Gene Ontology
• Cross-domain

21
Semantic Depth of Resource Descriptions

• Dictionaries and Vocabularies
• the schemas developed at this level define simple
  lists of concepts and their definitions
• Taxonomies
• their characteristic is that the main relation
  they define between concepts is that of
  specialization
• Thesauri
• besides defining relations among broader/narrower
  terms through the definition of hierarchies, a
  thesaurus also declares relations of equivalence,
  association and synonymy
• Reference Models
• comprise a representation vocabulary for
  referring to the concepts in the subject area and
  the logical statements that describe the nature
  of the terms, the relations among the terms and
  the way the terms can or cannot be related to
  each other

22
Ontologies - What Are They?
Thesauri narrower term relation
Frames (properties)
Formal is-a
General Logical constraints
Catalog/ ID
Informal is-a
Formal instance
Disjointnes, Inverse, part-of
Terms/ glossary
Value Restrs.
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A First Classification of RDF Schemas
Application Domain Dictionary/ Vocabulary Taxonomy Thesaurus Reference Model
Cultural Heritage/Archives/Libraries Euler RSLP-CLD CIDOC
Educational/ Academic IMS Universal Mathematics International CERIF
Publishing/ News BibLink SLinkS RSS
Audio-Visual IMDB
Geospatial/ Environmental CZM GML
Biology/ Medicine Gene
E-Commerce BSR UNSPSC RED
Ubiquitous/ Mobile/Grid Computing CC/PP RDF Calendar SAO
Cross-Domain CERES/NBII Dublin Core WordNet Metanet Limber Thesaurus Top Level Ontology
24
Outline

• Database issues for RDF metadata management
• The Data Independence Issue
• The Query Language Issue
• The Model Issue
• RDF Query Language RQL
• Querying Large RDF Schemas
• Filtering/Navigating Complex RDF
• descriptions
• Storing Voluminous RDF descriptions
• Alternative DB representations
• Performance Figures
• The ICS-FORTH RDFSuite
• Conclusions and remaining issues

25
The Data Independence Issue

• Conceptual Level Describing resources using one
  or several RDF schemas
• Logical Level How RDF descriptions and schemas
  are physically stored
• Logical-schema Data organization using tables,
  objects, etc.
• Physical-schema Data organization using files,
  records, indices, etc.
• RDF data independence is crucial for ensuring
  scalability of real-scale Semantic Web
  applications

26
The Query Language Issue
Querying the Semantics (RQL)
Querying the Structure (Squish)
Querying the Syntax (XQuery)
27
Why a Data Model for RDF ?

• As support for physical/logical independence
• RDF can be stored in files, a native repository,
  a relational database
• RDF can be virtual, as a view of a repository,
  integrated sources
• RDF can be in memory, using data structures in C,
  C, Java, etc
• RDF can be streamed between processes
• To describe information content of RDF Statements
• to agree and reason about information content,
  preservation
• To define semantics of a data manipulation
  language
• A query language describes in a declarative
  fashion, the mapping between an input instance of
  the data model to an output instance of the data
  model

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But RDF has specifics Serialization syntax

• XML attributes vs elements for RDF properties
• fname, lname
• XML flat vs nested structures of RDF statements
• Description vs. Painter elements
• RDF properties are unordered, optional, and
  multivalued
• 2 paints and 0 creates
• One more motivation for a data model
• isolate the user from syntactic aspects of RDF/XML

ltrdfDescription rdfIDpicasso132" fnamePablo
lnamePicassogt ltpaints rdfresource"http//
museoreinasofia.mcu.es/guernica.gif"/gt
ltpaints rdfresource"http//www.artchive.com/woma
n.jpg/gt ltrdftypegtPainterlt/rdftypegt lt/rdf
Descriptiongt ltrdfDescription rdfabout
"http//museoreinasofia.mcu.es/guernica.gif"gt
ltrdftypegtPaintinglt/rdftypegt
ltcreatedgt1937lt/createdgt lt/rdfDescriptiongt
ltrdfDescription rdfabout " http//www.artchive.
com/woman.jpg"gt ltrdftypegtPaintinglt/rdftypegt
ltcreatedgt1904lt/createdgt lt/rdfDescriptiongt
ltPainter rdfIDpicasso132"gt
ltfnamegtPablolt/fnamegt ltlnamegtPicassolt/lnamegt
ltpaintsgt ltPainting rdfabout"http//w
ww.artchive.com/woman.jpg/gt
ltcreatedgt1904lt/createdgt lt/paintsgt
ltpaintsgt ltPainting rdfabout"http//museo
reinasofia.mcu.es/guernica.gif"gt
ltcreatedgt1937lt/createdgt lt/Paintinggt
lt/paintsgt lt/Paintergt
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But RDF has specifics Schema Semantics
ltrdfsClass rdfID"Artist"/gt ltrdfsClass
rdfID"Artifact"/gt ltrdfssubClassOf
rdfresource"Artist"/gt lt/rdfsClassgt ltrdfsClas
s rdfID"Painter"gt ltrdfssubClassOf
rdfresource"Artist"/gt lt/rdfsClassgt ltrdfsClas
s rdfID"Painting"gt ltrdfssubClassOf
rdfresource"Artifact"/gt lt/rdfsClassgt ltrdfPro
perty rdfID"fname"gt ltrdfsdomain
rdfresource"Painting"/gt ltrdfsrange
rdfresource
http//www.w3.org/rdf-
datatypes.xsdString"/gt lt/rdfPropertygt
ltrdfProperty rdfID"creates"gt ltrdfsdomain
rdfresource"Artist"/gt ltrdfsrange
rdfresource"Artifact"/gt lt/rdfPropertygt ltrdfP
roperty rdfID"paints"gt ltrdfsdomain
rdfresource"Painter"/gt ltrdfsrange
rdfresource"Painting"/gt ltrdfssubPropertyOf
rdfresource"creates"/gt
lt/rdfPropertygt ltrdfProperty rdfID"created"gt
ltrdfsdomain rdfresource"Painting"/gt
ltrdfsrange rdfresource
http//www.w3.org/rdf-
datatypes.xsdDate"/gt lt/rdfPropertygt

• Distinguish between labels of nodes and edges
• Painter vs. paints
• Class and properties are organized in subsumption
  hierarchies
• Painter lt Artist
• Properties are inherited
• r6 may also have a creates property
• References are typed
• r2 should be of class lt Painting
• Literal values are typed
• 1937 is not a string but a date value !

30
But RDF has specifics Superimposed Descriptions
rdftype
rdftype

• Resources may belong to multiple (unrelated
  though isa) classes
• r2 is both a Painting and an ExtResource
• Heterogeneous descriptions reminiscent of SGML
  exceptions
• What is the structure of Painting resources?

31
RDF/S vs. Well-Known Formalisms

• Relational or Object Database Models (ODMG, SQL)
• Classes dont define table or object types
• Instances may have associated quite different
  properties
• Collections with heterogeneous members
• Semistructured or XML Data Models (OEM, UnQL,
  YAT, XML Schema)
• Labels only on nodes or edges
• Class and property subsumption is not captured
• Heterogeneous structures reminiscent to SGML
  exceptions
• Knowledge Representation Languages (Telos, DL,
  F-Logic)
• Absence of complex values and n-ary
  relationships (bags, sequences)

32
A Semistructured Data Model for RDF

• Graph based, unordered, edge/node-labeled (in the
  style of OEM)
• But what about sequences (ordered)?

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Towards a Formal Data Model for RDF

• An RDF schema is a 5-tuple RS (VS, ES, H, ?,
  ?)
• VS a set of nodes
• ES a set of edges
• ? (?,lt) a well-formed hierarchy of names
• ? an incidence function Es ? Vs?Vs
• ? a labeling function VS ? ES ?? ??
• An RDF description base, instance of a schema RS,
  is a 5-tuple RD (VD, ED, ?, ?, ?)
• VD a set of nodes
• ED a set of edges
• ? an incidence function ED ? VD?VD
• ? a valuation function VD ? V
• ? a labeling function VD ? ED ?2???
• ? u ? VD, ? ? n ? C?T ?(u) ?n
• ? e ? ED u,u, ? ? p

34
Why a Type System for RDF ?

• For error detection safety
• to verify that statements comply to what the
  application expects
• to make sure that the application accesses valid
  statements
• to enforce safe operations (e.g., dont do float
  arithmetic on classes!)
• to check that compositions of operations make
  sense
• For performance
• to design storage (saving space, improving
  clustering, etc.)
• to process queries (algebraic laws, rewriting
  path expressions, etc.)
• We need a full-fledged Data Definition Language
  for RDF !
• RDF Schema is viewed more as an ontology
  modeling tool

35
Towards a Type System for RDF

• Type System
• ? ?L ?U ? ? (1? 2? n?)
• Interpretation Function
• Literal types, ?L dom(?L)
• Bag types, ? ?1, ?2,, ?n, ?1, ?2,,
  ?n ? V are values of type ?
• Seq types, ? ?1, ?2,, ?n, ?1, ?2,,
  ?n ? V are values of type ?
• Alt types, (1?1 2?2 n?n ) ?I, ?i
  ? V, 1ltiltn is a value of type ?i
• c ? C, c ? ? ? ?(c)??(c) c lt c
• p ? P, p ?1, ?2 ?1 ? domain(p), ?2
  ? range(p)??(p) p lt p

36
A Formal Data Model for RDF/S
H
Property
Class
lt
lt
?
N
?
?
?L
?
?C
T
?P
?
?
.
.
V
val,val
U
val
?
?
URI
?
S
resources
containers
literals
37
Schema Constraints

• Class, Property and Type names are mutually
  exlusive
• C ? P ? T ?
• Literal, Resources and Container values are
  mutually exclusive
• L ? U ? V/U, L ?
• ? c, c? ? C
• Class is the root of class hierarchy
• c lt Class
• subClassOf relation is transitive
• c lt c?, c?lt c? ? ? c lt c? ?
• subClassOf relation is antisymmetric
• c lt c? ? c ? c?
• Domain and range of properties should be defined
  and they should be unique
• ?p ? P, ?!c1 ? C (c1 domain(p))
  ? ?!c2 ?C ? TL (c2range(p))

• ? p, p?, p?? ? P
• Property is the root of property hierarchy
• p lt Property
• subPropertyOf relation is transitive
• plt p?, p?lt p? ? ? plt p? ?
• subPropertyOf relation is antisymmetric
• p lt p? ? p ? p?
• If p is subPropertyOf of p? then domain of p is
  subset of domain of p? and range of p is subset
  of range of p?
• p lt p ?? domain(p) ? domain(p?) ?
  range(p) ? range(p?)
• A reified statement should have exactly one
  rdfpredicate, rdfsubject and rdfobject property

38
Data Constraints

• For all values ?u ? V
• If u is a URI then it is an instance of one or
  more Classes
• u ? U ? ?(u) ? C
• If u is a literal then it an instance of one
  and only one Literal type
• u ? L ? ?(u) ? TL
• If u is a container then it an instance of one
  and only one Container type
• u ? V/U, L ? ?(u) ? TB S A

• For all properties ?p ? P, u1,u2 ? p
• if p belongs to the set 1, 2, 3 then u1 is an
  instance of either rdfBag or rdfSeq or rdfAlt
• if p ?1, 2, 3? ?(u1)?TB S A
• if p doesnt belong to 1, 2, 3, then u1
  belongs to the domain of p and u2 belongs to
  the range of p
• if p ? P/1, 2, 3 ? ?(u1) ? domain(p) ? ?(u2)
  ? range(p)

39
Querying RDF Descriptions An Introduction to RQL
40
The RDF Query Language (RQL)

• Declarative query language for RDF description
  bases
• relies on a typed data model (literal container
  types union types)
• follows a functional approach (basic queries and
  filters)
• adapts the functionality of XML query languages
  to RDF, but also
• treats properties as self-existent individuals
• exploits taxonomies of node and edge labels
• allows querying of schemas as semistructured data
• Relational interpretation of schemas resource
  descriptions
• Classes (unary relations)
• Properties (binary relations)
• Containers (n-ary relations)

41
A Cultural Community Resource Description Example
Portal Schema
Portal Resource Descriptions
r2 museoreinasofia.mcu.es/ guernica.jpg
r1www.rodin.fr/ thinker.gif
r4museoreinasofia.mcu.es
r3www.artchive.com/ woman.jpg
Web Resources
42
Querying Large RDF Schemas with RQL

• Basic Class Queries
• topclass
• subclassof(Artist)
• subclassof(Artist)
• superclassof(Painter)
• superclassof(Painter)

• Basic Property Queries
• topproperty
• subpropertyof(creates)
• subpropertyof(creates)
• superpropertyof(paints)
• superpropertyof(paints)
• domain(creates)
• range(creates)

• Querying the RDF/S meta-schema
• Class
• Property
• Literal

43
Class Property Querying

• Find the domain and range of the property creates
• seq ( domain(creates), range(creates) )
• while thanks to functional composition we can
  express
• subclassof ( seq ( domain(creates),
  range(creates) ) 0 ) or
• select X from
• subclassof(seq(domain(creates),
  range(creates))0) X

• Which classes can appear as domain and range of
  property creates
• select X, Y from XcreatesY or
• select X, Y from ClassX, ClassY,
  XcreatesY

• Find all properties defined on class Painting and
  its superclasses
• select _at_P, range(_at_P) from Painting_at_P or
• select P, range(P) from PropertyP where
  domain(P)gtPainting

44
Schema Navigation using RQL

• Iterate over the subclasses of class Artist
• select X from ArtistX or
• select X from subclassof(Artist)X
• Find the ranges of the property exhibited which
  can be reached from a class in
  the range of property creates
• select Y, Z from createsY.exhibitedZ
• Find the properties that can be reached from a
  range class of property creates, as well as,
  their respective ranges
• select from createsY._at_PZ or
• from ClassY, (Class union Literal)Z,
  createsY._at_PZ

45
Exporting Schemas using RQL Queries

• Find Leaf Classes (i.e., classes without
  subclasses)
• select C1
• from ClassC1
• where not ( C1 in (select C1
• from ClassC2
• where C2 lt C1) )
• Find all schema information (i.e., group related
  superclasses and properties for each class)
• select C, superclassof(C), (select P, range(P)
• from
  PropertyP
• where
  domain(P) C)
• from ClassC

46
Querying Complex RDF Descriptions with RQL

• Find all resources
• 
  Resource
• Find the resources in the extent of the property
  creates
• creates or
• select from XcreatesY
• Find the resources of type ExtResource and
  Sculpture
• ExtResource intersect Sculpture
• ExtResource minus Sculpture
• ExtResource union Sculpture

47
Navigating in Description Graphs using RQL

• Find the Museum resources that have been modified
  after year 2000 (i.e., data path with node and
  edge labels)
• select X
• from MuseumX.last_modifiedY
• where Y gt 2000-01-01
• Find the resources that have been created and
  their respective titles (i.e., data path using
  only edge labels)
• select X, Z from createsY.titleZ
• Find the titles of exhibited resources that have
  been created by a Sculptor (i.e., multiple data
  paths)
• select Z, W
• from Sculptor.createsY.exhibited
  Z, ZtitleW

48
Using Schema to Filter Resource Descriptions

• Find the Painting resources that have been
  exhibited as well as the related target resources
  of type ExtResource (i.e., restrict multiply
  classified property target values using node
  labels)
• select X, Y from XPaintingexhibitedY.ExtResou
  rce
• Note the difference with the following path
  exression
• select X, Y from XPaintingexhibitedYExtResour
  ce
• Find modified resources which can be reached by a
  property applied to the class Painting and its
  subclasses (i.e., restrict property source values
  using edge labels)
• select _at_P, Y, Z
• from X_at_P.Ylast_modifiedZ
• where X ltPainting

49
Discover the Schema of RDF Descriptions

• Find the description of a resource with URI
  http//www.museum.es
• select X, (select _at_P, Y
• from Z Z _at_P Y
• where X Z and X Z)
• from X X
• where X http//www.museum.es
• Find the descriptions of resources whose URI
  match www.museum.es
• select X, (select W, (select _at_P, Y
• from Z Z
  _at_P Y
• where W Z and
  W Z)
• from W W
• where W X)
• from Resource X
• where X like "www.museum.es"

50
And if you still like triples

• Find the description of resources which are not
  of type ExtResource
• (
• (select X, _at_P, Y from X _at_P Y)
• union
• (select X, type, X from X X)
• )
• minus
• (
• (select X, _at_P, Y from XExtResource_at_PY)
• union
• (select X, type, ExtResource from ExtResource
  X)
• )

51
Comparing RQL to W3C XQuery

• Find the names of those who have created
  artifacts which are exhibited in Museums, along
  with the Museum titles
• RQL
• select Y, Z, V, R
• from Xcreates.exhibitedY.titleZ,
  
• Xfirst_nameV,Xlast_nameR

52
Comparing RQL to W3C XQuery
53
Comparing RQL to W3C XQuery

• XQuery
• LET t document("sirpac-culture-merged.rdf")//
  description
• FOR artist IN rdfinstance-of-class(t,
  rdfpredicate-domain(t, "creates"))
• LET artifact rdfjoin-on-property(t,
  artist,"creates"),
• museum rdfjoin-on-property(t,
  artifact, "exhibited")
• RETURN
• ltresultgt
• filter(artist artist/last_name
  artist/first_name),
• filter(museum museum/title)
• lt/resultgt

54
Comparing RQL to W3C XQuery
55
Comparing RQL to W3C XQuery

• XML syntactic and schematic discrepancies of
  semantically equivalent RDF statements
• normalized representation under the form of
  merged descriptions
• XQuery has no built-in knowledge of the RDF
  schema information
• function library that exploits the RDF schema if
  the assertions of the schema are also present in
  the normalized representation
• Data model mismatches between XML and RDF impact
  type safety of functions and queries
• bag( range(Artist) ) union subclassof(Artifact)
• In RQL Type Error
• In XQuery All the subclasses of Artifact !

56

• Storing RDF Descriptions RSSDB Preliminary
  Performance Results

57
Modeling the ODP Catalog with RDF/S
rdf http//www.w3.org/1999/02/22-rdf-syntax-ns r
dfs http//www.w3.org/2000/01/rdf-schema
related
Class
ns1 http//www.dmoz.org/topic.rdf
Recreation
Regional
Paris
Lodging
Travel
Vacation- Rentals
Hotel
Ile-de-France
related
Hotel Directories
r2
r4
r1
r3
r1 http//www.sunscale.com/france/paris/index.ht
m
58
ODP Statistics

• ODP Version 16-01-2001
• 170 Mbytes of class hierarchies
• 700 Mbytes of resource descriptions
• 337,085 topics
• 16 hierarchies with
• max depth 13 ( 6.86 on average)
• max subclasses 314 ( 4.02 on average)
• 2,342,978 URIs

59
Generic Representation
Resources
Triples
uri text
id int
predid int
subid int
objid int
objvalue text
1
http//www.dmoz.org/topics.rdfsHotel
6
2
1
2
http//www.dmoz.org/topics.rdfsHotel
Directories
5
3
7
3
http//www.oclc.org/dublincore.rdfstitle
5
1
8
4
http//www.dmoz.org/schema.rdfExt.Resource
5
9
2
5
http//www.w3.org/1999/02/22-rdf-syntax-nstype
3
9
SunScale
6
http//www.w3.org/2000/01/rdf-schemasubClassOf

7
http//www.w3.org/1999/02/22-rdf-syntax-nsPropert
y
8
http//www.w3.org/2000/01/rdf-schemaClass
r1
9
60
Specific Representation
Namespace
Type
idint
uri text
id int
nsid int
lpart text
1
http//www.w3.org/2000/01/rdf-schema
1
1
Resource
2
2
Bag
2
http//www.w3.org/1999/02/22-rdf-syntax-ns
3
http//www.oclc.org/dublincore.rdfs
3
2
Seq
4
http//www.dmoz.org/topics.rdfs
4
String
Property
Class
id int
nsid int
lpart text
rangeid int
nsid int
lpart text
domainid int
id int
11
5
Ext.Resource
4
14
3
title
1
15
4
12
3
description
1
4
Hotel
13
4
Hotel Directories
16
5
title
11
4
SubClass
SubProperty
subid int
superid int
subid int
superid int
11
1
16
14
12
1
13
12
61
DBMS Size vs. Schema Triples

• DBMS size scales linearly
  with the number of schema triples

62
DBMS Size vs. Data Triples

• DBMS size scales linearly
  with the number of data triples

63
Query Templates for RDF description bases
64
Execution Time of RDF Benchmark Queries
65
Comparison

• Specific Representation permits the customization
  of the database representation of RDF metadata
• Specific Representation outperforms the Generic
  Representation for all types of queries
• Q1, Q2, Q5, Q7, Q10, Q11 by a factor up to 3.73
• Q3, Q4, Q6 by a factor up to 2.8
• Q8, Q9 by a factor up to 95,538
• Generic representation pays severe penalty for
  maintaining large tables (Triples, Resources)
• e.g., queries Q8, Q9 require (self-) joins of
  Triples, Resources

66
Other Issues

• RDF Metadata Generation from Legacy Repositories
• need to capture schemas from heterogeneous
  resources
• RDF Schema Evolution and Metadata Revision
• to support the dynamics of resource descriptions
• RDF Repositories Distribution
• for integration with WebDAV or LDAP-like
  architectures
• RDF Query Languages Optimization
• for real-scale Semantic Web applications

67
The ICS-FORTH RD Activities on the Semantic Web
68
The C-Web Project

• EC IST Project (13479) 1999-2000
• Overall Aim Set-up methodologies and
  infrastructure for fast deployment and easy
  management of Web Portals for communities
  requiring
• effective knowledge assimilation,elicitation
• efficient query answering
• Partners INRIA(FR), FORTH(GR), EDW(IT)
• Running Application Scenario Learning Portals
  for intranets or the Internet
• Corporate Knowledge Servers (e.g., automobile,
  telecommunications)
• Memory Organizations (e.g., museums, libraries,
  archives)

69
Project MESMUSES

• Programme(IST) KAIII.1.4. Multimedia Content and
  Tools (Access to digital collections of cultural
  and scientific content)
• Contract IST-2000-26074 (02/2001 07/2003)
• Partners INRIA (France),
• FINSIEL - Multimedia Services (Italy),
• ICS-FORTH (Hellas),
• ENSTB - Ecole Nationale Supérieure des
  Télécommunications - Bretagne (France), VALORIS -
  Group, Paris (France)
• IMSS - Istituto e Museo di Storia della
  Scienza, Firenze (Italy)
• CSI - Cité des Sciences et de l'Industrie,
  Paris (France)
• EDW International, Milano (Italy)
• DET-UNIFI - University of Florence (Italy)

70
C-Web Architecture
Artist
Artist
Painting
Client Tier
Museum
URL
Query Browsing Interface
Painter
Resource Description Interface
Schema Generator
http
XML/XSL
RQL
RDF/XML Schema
RDF/XML Descriptions
CWEB/Application Server
Middleware APIs
Session Manager
Logical Middle Tier
Metadata Store
RDF/XML Loader
XML/XSL Processor
Query Engine
URL Resolver
XML
XML
XML
Resources
http
XML Wrapper

Well-formed
XML enabled DBMS
Other docs
XML docs
on the Intranet
on the Web
e.g. mails,
news, reports
71
The C-Web Metadata Middleware
PARENT PROCESS
APACHE
CHILD PROCESS ID
PHP
TOMCAT
VRP
COCOON
DB CONNECT
LOADER
RQL server
PostgreSQL Server
72
The ICS-FORTH RDFSuite High-level and Scalable
Tools for the Semantic Web http//139.91.183.309
090/RDF/
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(No Transcript)
74
The RDFSuite Main Components

• The Validating RDF Parser (VRP) Karsten Tolle
  Diploma Thesis
• The First RDF Parser supporting semantic
  validation of both resource descriptions and
  schemas
• The RDF Schema Specific DataBase (RSSDB) Sophia
  Alexaki MSc. Thesis
• The First RDF Store using schema knowledge to
  automatically generate an Object-Relational
  (SQL3) representation of RDF metadata and load
  resource descriptions
• The RDF Query Language (RQL) Greg Karvournarakis
  MSc. Thesis
• The First Declarative Language for uniformly
  querying RDF schemas and resource descriptions

75
The RDFSuite Architecture
ICS-RSSDB
ICS-VRP
ICS-RQL Interpreter
Class
Property
Typing
p_name
domain
range
c_name
LIB C
Graph Constructor




Loading RDF Java APIs
DBMS RDF query API
JDBC
RDF Loader
VRP
Internal
SQL3 SPI functions
SubClass
RDF Model
SubProperty
SQL3
SQL3
Evaluation
Parser
class1
property
URI
creates
76
Validating RDF Parser (VRP)

• Syntactic Validation
• RDF/XML syntax described in the RDF MS
  Specification
• Semantic Validation
• Semantic constraints derived from the RDF Schema
  Specification
• Implementation
• Standard compiler generator tools for Java CUP
  (0.1) JFLEX (1.3.2)
• 100 Java(TM) development (Java 1.2.2)

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VRP Interface
78
VRP Features

• Understands embedded RDF in HTML or XML
• Full XML Schema Data Types support
• Full Unicode support
• Statement validation across several RDF/XML
  namespaces
• Persistent namespaces (for consistency,
  optimization)
• Various Output Options
• Debugging
• Serialization in files under the form of triples
  and graphs
• Statistics for schema characteristics
  (class/property hierarchies) and resource
  distribution (class population)
• Easy to use as a standalone application
• No other software needs to be installed (e.g.,
  XML Parsers)
• Easy to integrate with other applications e.g.,
  visualization tools
• RDF Model Construction and Validation Java APIs

79
RDF Schema Specific DataBase (RSSDB)

• Persistent RDF Store using standard database
  technology
• Separates schema form data information
• Distinguishes between classes and properties
• Preserves the flexibility of RDF in
• Refining schemas
• Enriching descriptions
• Using multiple schemas
• Implementation
• On top of an object-relational DBMS (SQL3) like
  PostgreSql
• Using JDBC Interface (2.0)

80
The RDF to DBMS Loader

• Resource
• URI

Extended VRP Validator
RDF Model

• RDF_Resource
• rdftype
• ...

P1
Persistent Namespace (DBMS)
Additional Constraints
C2
C1
store()
P1
r1
r2
RDF Querying APIs

• RDF_Statement
• rdfpredicate
• rdfsubject
• rdfobject

• RDF_Property
• rdfsdomain
• rdfsrange
• rdfssubPropertyOf
• link_list

• RDF_Class
• rdfssubClassOf

store()
store()
RDF Loading APIs
DBMS
r1
81
RSSDB Interface
82
RSSDB Features

• Customization of the database representation
  according to
• Employed meta-schemas (RDF/S, DAML-OIL)
• RDF schemas and description bases peculiarities
  (number of classes vs. properties, resource
  distribution per classes)
• Query functionality of applications
• Scalability
• size of DBMS scales linearly with the number of
  loaded triples (tested with the Open Directory
  Portal comprising about 6 million triples)
• incremental loading of voluminous description
  bases
• Easy to use as a standalone application
• Requires only JDBC-compliant ORDBMS
• Easy to integrate with other applications e.g.,
  metadata servers
• RDF Model Loading Update Java APIs

83
RDF Query Language (RQL)

• Declarative language (like ODMG OQL) for
  conceptual browsing querying of voluminous RDF
  Description Bases
• Easy navigation and resource discovery (using few
  query terms)
• Task-specific personalization of RDF description
  bases (views)
• Seamless querying of RDF schemas and resource
  descriptions
• Flexible export facilities of RDF metadata
  (restructuring)
• RQL fully supports
• XML Schema data types (for filtering literal
  values)
• grouping primitives (for constructing complex XML
  results)
• aggregate functions (for extracting statistics)
• recursive traversal of class and property
  hierarchies (for matchmaking)
• Implementation
• C development (GCC 2.95.1) on top of an ORDBMS
  (Unix, Linux)
• Client/Server architecture (XDR-based)

84
The RDF Query Interpreter (RQL)
Syntax analysis
Query string

• Syntactical analysis (lex/yacc)
• CNF transformation

(1)
Type inference
Syntax tree under CNF

• Checks type compatibility
• Sets appropriate evaluation functions

(2)
Query string
(3)
Main
Graph construction
Query result
(4)

• Evaluation of dependencies
• Factorization functions

Query graph
Typing
DBMS RDF Query APIs
(5)
Query graph
Evaluator
Evaluation
(6)

• Defines evaluation functions
• Query Processing

Result
DBMS
85
RQL Web Interface
86
RQL Features

• Pushes as much as possible query evaluation to
  the underlying DBMS
• Benefit from robust SQL3 query engines
• Extensive use of DB indices
• Generic RDF/XML result form (Containers)
• Standard XSL/XSL processing for customized
  rendering
• Easy to couple with commercial ORDBMSs (Oracle,
  DB2)
• RDF querying APIs (SQL3/C functions)
• Easy to integrate with different Application
  Servers (Zope, JetSpeed)
• C or Java drivers to RQL servers
• Easy to learn and use
• One day training

87
RDFSuite Summary

• RDFSuite addresses the needs of effective and
  efficient RDF metadata management by providing
  tools for validation, storage and querying
• validation follows a formal data model and
  constraints enforcing consistency of RDF schemas
• scalability
• declarative query language for schema and data
  querying
• Ongoing efforts
• RQL query optimization
• RQL update and transactional aspects

88
Thank you
Hvala
Danke
Merci
Gracias
Grazie
89
University Portals

• Most of the Corporate Portal features apply to
  higher education
• uPortal is bridging the gap between corporate
  portals and the needs of Higher Education
  Institutions
• One of the most complex portal applications is
  instruction. Several information channels have to
  be synchronized together to
• present learning materials and assessments
• monitor the learners progress and adapt the
  presentation to the learners knowledge
• audit the progression through content
• and perhaps even simulate a process

90
The Evolving Campus
91
The 21Century Campus in the .com World
92
The Higher Education Web World
93
uPortal Hierarchy
94
uPortal Interfaces

• Authentication
• Proving your identity
• Authorization
• Deciding what you can access
• Directory services
• Such as populating EduPerson
• User preferences
• Profiles, structure, themes, skins
• Channel information
• Availability and configuration