Tutorial

1
Tutorial 5Scientific Data Integration and
Mediation
Bertram Ludäscher Ilkay Altintas Amarnath
Gupta Kai Lin
San Diego Supercomputer Center U.C. San Diego
2
Acknowledgements

• National Science Foundation (NSF)
• www.nsf.gov
• GEOsciences Network (NSF)
• www.geongrid.org
• Biomedical Informatics Research Network (NIH)
• www.nbirn.net
• Science Environment for Ecological Knowledge
  (NSF)
• seek.ecoinformatics.org
• Scientific Data Management Center (DOE)
• sdm.lbl.gov/sdmcenter/

3
Outline

• 830 1030am Tutorial Data Integration
  Mediation
• Introduction to database mediation
• motivation and architecture
• XML-based data integration
• Database mediation theory primer
• logic view definitions, view unfolding, computing
  feasible plans
• From XML-based to Knowledge-based mediation
• use of ontologies in data integration, ...
• 1030 1045am BREAK
• 1045 1200 Applications and Demos
• 1045 1105 Mediator Demo
• 1105 1120 Queries w/ Ontology Support
• 1120 1140 Scientific Workflows
• 1140 1200 KNOW-ME Ontology Tool

4
Information Integration Challenges

• System aspects Grid Middleware
• distributed data computing
• Web Services, WSDL/SOAP,
• sources functions, files, databases,
• Syntax Structure
• XML-Based Mediators
• wrapping, restructuring
• XML queries and views
• sources XML databases
• Semantics
• Model-Based/Semantic Mediators
• conceptual models and declarative views
• SemanticWeb/KnowledgeGrid stuff ontologies,
  description logics (RDF(S), DAMLOIL, OWL ...)
• sources knowledge bases (DBCMsICs)

5
Information Integration from a DB Perspective

• Information Integration Problem
• Given data sources S1, ..., Sk (DBMS, web sites,
  ...) and user questions Q1,..., Qn that can be
  answered using the Si
• Find the answers to Q1, ..., Qn
• The Database Perspective source database
• Si has a schema (relational, XML, OO, ...)
• Si can be queried
• define virtual (or materialized) integrated
  views V over S1 ,..., Sk using database
  query languages (SQL, XQuery,...)
• questions become queries Qi against V(S1,..., Sk)

6
Standard (XML-Based) Mediator Architecture
wrappers implemented as web services
7
Some BIRNing Data Integration Questions
Biomedical Informatics Research
Network http//nbirn.net

• Data Integration Approaches
• Lets just share data, e.g., link everything from
  a web page!
• ... or better put everything into an relational
  or XML database
• ... and do remote access using the Grid
• ... or just use Web services!
• Nice try. But
• Find the files where the amygdala was
  segmented.
• Which other structures were segmented in the
  same files?
• Did the volume of any of those structures differ
  much from normal?
• What is the cerebellar distribution of rat
  proteins with more than 70 homology with human
  NCS-1? Any structure specificity? How about other
  rodents?

8
An Online Shoppers Information Integration
Problem
El Cheapo Where can I get the cheapest copy
(including shipping cost) of Wittgensteins
Tractatus Logicus-Philosophicus within a week?
One-World Mediation
9
A Home Buyers Information Integration Problem
What houses for sale under 500k have at least 2
bathrooms, 2 bedrooms, a nearby school ranking
in the upper third, in a neighborhood with
below-average crime rate and diverse population?
Multiple-Worlds Mediation
10
A Geoscientists Information Integration Problem
What is the distribution and U/ Pb zircon ages of
A-type plutons in VA? How about their 3-D
geometry ? How does it relate to host rock
structures?
Complex Multiple-Worlds Mediation
11
A Neuroscientists Information Integration Problem
Biomedical Informatics Research
Network http//nbirn.net
What is the cerebellar distribution of rat
proteins with more than 70 homology with human
NCS-1? Any structure specificity? How about other
rodents?
Complex Multiple-Worlds Mediation
12
Structural / XML-Based Mediation
13
Abstract XML-Based Mediator Architecture
USER/Client
Query Q o V (S_1,...,S_k)
Integrated XML View V
Integrated View Definition IVD(S1,...,Sn)
MEDIATOR
XML Queries Results
XML View
XML View
XML View
Wrapper
Wrapper
Wrapper
S_1
S_2
S_k
14
Extensible Markup Language (XML)
... in their wonderful book called lttitlegtSemWeb
Tractat lt/titlegt by B. Schatz and T.B. Lee, the
authors show how ...
... in their wonderful book called lttitlegtSemWeb
Tractatlt/titlegt by ltauthorgtB. Schatzlt/authorgt and
ltauthorgt T.B. Leelt/authorgt, the authors show how
...
... in their wonderful book called SemWeb Tractat
by B. Schatz and T.B. Lee, the authors show how
...
ltbookgt lttitlegtSemWeb Tractatlt/titlegt
ltauthorgtB. Schatzlt/authorgt ltauthorgtT.B.
Leelt/authorgt lt/bookgt

• (meta)language for marking up text data with
  user-definable tags
• (X)HTML, XSLT, XML Schema, ...
• MathML, BioML, GeoML, NeuroML, ...
• XML-RPC, SOAP, ...
• semistructured tree data model
• flexible marked-up text, web-pages, databases,
  ...
• container model
• boxes within boxes

15
Example Relational Data gt XML
R
?R? ?tuple? ?A? a1 ?/A? ?B? b1 ?/B? ?C? c1
?/C? ?/tuple? ?tuple? ?A? a2 ?/A? ?B? b2
?/B? ?C? c2 ?/C? ?/tuple? ?/R?
16
Tag Names Nesting gt XML DTDs (Grammars)
17
XML DTDs vs. XML Schema

• XML DTDs
• set of allowed tag names
• their nesting structure (via grammar rules)
• XML Schema
• tag names and nesting structure
• user-defined complex data types
• subtyping (no multiple inheritance) RESTRICT and
  EXTEND
• separate namespace for type names and tag
  (element) names
• ...

18
XML Schema User-Defined Type/Class Hierarchy
19
XML Schema Declarations (home-style syntax)
Complex Type Declarations
20
XML Schema (home-style)
Simple Type Declarations
Complex Types
21
XML Schema Substitution Groups
Elements of a substitution group (hexagons) and
associated complex types (boxes)
22
XML Schema Declarations (W3C syntax)
23
XML Query Languages

• XPath
• root//books/bookcover_stylepaperbackpric
  elt80
• XQuery
• the W3C XML query language
• XSLT
• XML transformations (XMLgtHTML, XMLgtXML)
• ...

24
Transforming and Rendering XML XSLT
25
XMAS XML Matching And Structuring language
Integrated View Definition Find books from
amazon.com and DBLP, join on author, group by
authors and title
26
Database Mediation Theory Primer
27
Mediator Query Processing
Query Q
Integrated View Definition V
Translator
parsed plan
Composition (Q o V)
composed plan
Rewriter/Optimizer
Compile-time
optimized plan
Run-time
Plan Execution
28
Logic View Definitions (Global-as-View)
orQuerying and Reasoning with the Family ...

• Warm up Who says this?
• Your are my son, but Im not your father!
• The mother!

29
Logic View Definitions (Global-as-View)

• Globals-as-View (GAV)
• Integrated view V is defined in terms of the
  sources Src_1, ... , Src_k
• Given the following source databases
• Src_1 schema father(Father,Child),
  mother(Mother,Child)
• Src_2 schema spouse(Spouse, Spouse)
• Src_3 schema male(Person), female(Person)
• Can you define integrated views V for ... ?
• parent(Parent,Child)
• short parent/2, i.e., table/relation name is
  parent, arity (columns) is 2
• son/2, daughter/2
• brother/2, sister/2
• brother_in_law/2, sister_in_law/2
• aunt/2, uncle/2
• married/2, bachelor/2

30
Logic View Definitions (Global-as-View)Source
relations father/2, mother/2, spouse/2, male/1,
female/1 ? , conjunction (and) ?
disjunction (or) ? not negation

• parent(C,P) ?
• father(C,P) mother(C,P) .
  
• son(P,S) ?
• parent(S,P) , male(S) .
• brother(X,B) ?
• parent(X,P), son(P,B), X ? B .
• brother_in_law(X,B) ?
• sister(X, Z), spouse(Z, B)
• spouse(X, Z), brother(Z, B) .

31
Logic View Definitions (Global-as-View)Source
relations father/2, mother/2, spouse/2, male/1,
female/1 ? , conjunction (and) ?
disjunction (or) ? not negation

• uncle(X, U) ?
• parent(X, Z), brother(Z, U)
• parent(X, Z), brother_in_law(Z,
  U) .
• aunt(X, A) ?
• parent(X, Z), sister(Z, A)
• parent(X, Z), sister_in_law(Z, A)
  .
• married(X) ?
• spouse(X, _) .
• bachelor(X) ?
• person(X) , not married(X) .

32
Query Rewriting and Query Evaluation

• Query Rewriting
• - Given a user query Q in terms of virtual views
  V...
• - Find an equivalent query Q in terms of the
  sources Src_1,...,Src_k
• Query Evaluation
• - Given a query Q, evaluate Q over the source
  databases
• D Src_1 ? ... ? Src_k
• Examples
• Q_uncle/2 (X,Y) uncle(X,Y) holds in D
• Q_toms_uncle/1 X uncle(tom, X) holds in D
  
• Q_whose_uncle_is_tom/1 X uncle(X, tom)
  holds in D

33
Query Rewriting (for GAV)

• Query rewriting
• - Given a user query Q in terms of virtual views
  V...
• - Find an equivalent query Q in terms of the
  sources Src_1,...,Src_k
• Query Q, views V, source schemas S
• View unfolding
• starting with Q, repeatedly replace view
  predicates by the definition
• Creating a feasible plan
• here compute disjunctive normal form (DNF)
• DNF disjunction of conjunctions ( union of
  joins)
• order goals within each conjunction according to
  sources query capabilities

34
Example

• ?- plan(brother(X0,X1)) .
• brother(X0, X1)
• LQP gt
• (father(X0, X2) v mother(X0, X2))
• (father(X1, X2) v mother(X1, X2)) male(X1)
  neq(X0, X1)
• brother(X0, X1)
• NNF LQPgt
• (father(X0, X2) v mother(X0, X2))
• (father(X1, X2) v mother(X1, X2)) male(X1)
  neq(X0, X1)

35
Example (Contd)

• ?- plan(brother(X0,X1)) .
• brother(X0, X1)
• DNF LQPgt
• father(X0, X2)father(X1, X2)male(X1)neq(X0,
  X1)
• v mother(X0, X2)father(X1, X2)male(X1)neq(X0,
  X1)
• v father(X0, X2)mother(X1, X2)male(X1)neq(X0,
  X1)
• v mother(X0, X2)mother(X1, X2)male(X1)neq(X0,
  X1)

36
Example (Contd)

• ?- plan(brother(X0,X1)) .
• brother(X0, X1)
• Bp ordered LQPgt
• parentDb(father(X1, X2) father(X0, X2))
• genderDb(male(X1)) mediator(neq(X0, X1))
• v parentDb(father(X1, X2) mother(X0, X2))
• genderDb(male(X1)) mediator(neq(X0, X1))
• v parentDb(mother(X1, X2)father(X0,X2))
• genderDb(male(X1)) z_mediator(neq(X0, X1))
• v parentDb(mother(X1, X2)mother(X0, X2))
• genderDb(male(X1))z_mediator(neq(X0, X1))

37
Computing Feasible Plans (Goal Ordering)

• A conjunctive query Q is an expression of the
  form
• q( X ) ? p1( X1 ) , ..., pn( Xn )
• order of subgoals p_i is irrelevant
• An ordered plan P is an expression of the form
• q( X ) ? p1( X1 ) , ..., pn( Xn )
• order of subgoals p_i is important
• Problem
• given Q, compute P which is feasible, i.e.,
  observes the limited query capabilities of
  sources
• Here binding patterns, i.e., predicates
  arguments can be
• b bound
• f free
• _ bound or free

38
A Simple Algorithm for Ordering Goals
39
Query Containment

• A query Q1 is contained in Q2, denoted Q1? Q2
• if for all possible database instances, the set
  of answers to Q1 is contained in the set of
  answers to Q2.
• Q1 and Q2 are called equivalent
• if Q1 ? Q2 and Q2 ? Q1.
• Query containment is undecidable for many
  languages, e.g., for the relational calculus
  (SQL).
• For conjunctive queries, the problem is
  NP-complete (and thus decidable)
• Since query sizes tend to be small (in
  particular, when compared to database sizes),
  query containment is still of use in practice
  (indeed, it is one of the most fundamental tools
  for logic-based query optimization).

40
Query Containment

• Q1(Xs,Ys) is contained in Q2(Xs,Zs) iff
• ALL Xs (EXISTS Ys Q1(Xs,Ys)) ? (EXISTS Zs
  Q2(Xs,Zs))
• iff we can refute its negation
• iff
• NOT ALL Xs
• (EXISTS Ys Q1(Xs,Ys)) ? (EXISTS Zs Q2(Xs,Zs))
  
• iff
• EXISTS Xs (EXISTS Ys Q1(Xs,Ys))
• AND NOT (EXISTS Zs Q2(Xs,Zs))
• iff
• canonical_db(Q1) AND ? Q2(Xs,Zs)
• create database from Q1, then run Q2 as a
  query...

41
Query Containment Algorithm (in Prolog)

• Applications
• query minimization (conjunctive query is minimal
  if not conjunct can be dropped)
• semantic query optimization
• Q ? denial
• here denial is an integrity constraint and
  states what must not hold
• example denial false ? mother(X,M),
  father(Y,M)

42
Example

• 50 of the clauses of the executable plan are
  irrelevant ...

43
Mediator Demo

• Computer Science Challenges
• Given a query Q over virtual integrated database
  V, how to come up with Q over the source
  schemas? (cf. Garlic, DiscoveryLink, ...)
• query rewriting of Q(V) into Q(SRCs) using
  unfolding and normalization
• computation of feasible orders (NP-complete!?)
  while minimizing number of chunks sent to
  sources
• semantic query optimization (reasoning over
  plans!) e.g. conjunctive query containment is
  NP-complete Chandra-Merlin-77
• A Quick Demo of the current prototype
• Find 3D reconstructions of cells found in
  cerebellar cortex
• ?- ccdbData('cerebellar cortex').
• Join everything reachable along
  cerebellar-cortex.(has-a) in UMLS
• ....with concept markup in CCDB
• ... retrieve (links to) results
• ... also show on SmartAtlas tool

44
Mediator Demo
45
From XML-Based to Logic and Model-Based
(Semantic) Mediation
46
Whats the Problem with XML Complex
Multiple-Worlds?

• XML is Syntax
• DTDs talk about element nesting
• XML Schema schemas give you data types
• need anything else? gt write comments!
• Domain Semantics is complex
• implicit assumptions, hidden semantics
• sources seem unrelated to the non-expert
• Need Structure and Semantics beyond XML trees!
• employ richer OO models
• make domain semantics and glue knowledge
  explicit
• use ontologies to fix terminology and
  conceptualization
• avoid ambiguities by using formal semantics

47
From XML-Based to Model-Based Mediation

• Data and Knowledge Sharing Potential
• Database Mediation
• Knowledge Representation
• ________________________
• Model-Based Mediation
• Basic Ideas
• turn primary data sources into knowledge sources
• employ secondary glue knowledge sources
• generic UMLS, ...
• specific community/laboratory ontologies

48
Information Integration Landscape
49
Knowledge RepresentationRelating Theory to the
World via Formal Models
All models are wrong, but some are useful!
50
XML-Based vs. Model-Based Mediation
CM Descr.Logic, ER, UML, RDF/XML(-Schema),
CM-QL F-Logic, DAMLOIL,
51
Whats the Glue? Whats in a Link?

?
Y
X

• Syntactic Joins
• ?(X,Y) X.SSN Y.SSN equality
• ?(X,Y) X.UMLS-ID Y.UID
• Speciality Joins
• ?(X,Y,Score) BLAST(X,Y,Score) similarity
• Semantic/Rule-Based Joins
• ?(X,Y,C)
• X isa C, Y isa C, BLAST(X,Y,S), Sgt0.8
  homology, lub
• ?(X,Y,produces,B,increased_in)
• X produces B, B increased_in Y. rule-based
• e.g., X?-secretase, Bbeta amyloid,
  YAlzheimers disease
• Challenge
• compile semantic joins into efficient syntactic
  ones

52
Model-Based Mediation Methodology ...

• Lift Sources to export CMs
• CM(S) OM(S) KB(S) CON(S)
• Object Model OM(S)
• complex objects (frames), class hierarchy, OO
  constraints
• Knowledge Base KB(S)
• explicit representation of (hidden) source
  semantics
• logic rules over OM(S)
• Contextualization CON(S)
• situate OM(S) data using glue maps (GMs)
• domain maps DMs (ontology)
• terminological knowledge concepts roles
• process maps PMs
• procedural knowledge states transitions

53
... Model-Based Mediation Methodology

• Integrated View Definition (IVD)
• declarative (logic) rules with object-oriented
  features
• defined over CM(S), domain maps, process maps
• needs mediation engineers domain KRDB
  experts
• Knowledge-Based Querying and Browsing (runtime)
• mediator composes the user query Q with the IVD
• ... rewrites (Q o IVD), sends subqueries to
  sources
• ... post-processes returned results (e.g.,
  situate in context)

54
Model-Based Mediator Architecture
First results Demos KIND prototype, formal DM
semantics, PMs SSDBM00 VLDB00 ICDE01
NIH-HB01 (w/ Gupta, Martone)
55
Domain Maps (Ontologies) as Glue Knowledge Sources

• Domain Map Ontology
• representation of terminological knowledge
• Use in Model-Based Mediation
• (derived) concepts as drop points, anchor
  points, context for source classes
• compile-time use view definition, subsumption,
  classification,...
• runtime use querying/deduction, path queries,
  ....
• Formalisms
• Semantic nets, Thesauri, Frame-logic, Description
  logics, ...

56
Ontologies

• So what is an Ontology?
• definition of things that are relevant to your
  application
• representation of terminological knowledge
  (TBox)
• explicit specification of a conceptualization
• concept hierarchy (is-a)
• further semantic relationships between concepts
• abstractions of relational schemas, (E)ER, UML
  classes, XML Schemas
• Examples
• NCMIR ANATOM
• GO (Gene Ontology)
• UMLS (Unified Medical Language System
• CYC

57
Formalism for Ontologies Description Logic

• DL definition of Happy Father
  (Example from Ian Horrocks, U
  Manchester, UK)

58
Description Logic Statements as Rules

• In first-order logic (rule form)
• happyFather(X) ?
• man(X), child(X,C1), child(X,C2), blue(C1),
  green(C2),
• not ( child(X,C3), poorunhappyChild(C3) ).
• poorunhappyChild(C) ?
• not rich(C), not happy(C).

59
Description Logics

• Terminological Knowledge (TBox)
• Concept Definition (naming of concepts)
• Axiom (constraining of concepts)
• gt a mediators glue knowledge source
• Assertional Knowledge (ABox)
• the marked neuron in image 27
• gt the concrete instances/individuals of the
  concepts/classes that your sources export

60
Querying vs. Reasoning

• Querying
• given a DB instance I ( logic interpretation),
  evaluate a query expression (e.g. SQL, FO
  formula, Prolog program, ...)
• boolean query check if I ? (i.e.,
  if I is a model of ?)
• (ternary) query (X, Y, Z) I ?
  (X,Y,Z)
• gt check happyFathers in a given database
• Reasoning
• check if I ? implies I ? for all
  databases I,
• i.e., if ? gt ?
• undecidable for FO, F-logic, etc.
• Descriptions Logics are decidable fragments
• concept subsumption, concept hierarchy,
  classification
• semantic tableaux, resolution, specialized
  algorithms

61
Whats in an Answer?(Whats in a Link?
revisited)
?
Y
X

• Semantic/Rule-Based Joins
• ?(X,Y,produces,B,increased_in)
• X produces B, B increased_in Y. rule-based
• e.g., X?-secretase, Bbeta amyloid,
  YAlzheimers disease
• What is the Erdoes number of person P?
• 3
• Really? Why?
• authority based ltVIPgt said so
• faith based dont know but firmly believe
• query statement Q ... derived it from DB I
• query Q ... derived it from DB I and KB T using
  derivation D
• gt logic-based systems often come with
  explanations (computations as proofs)

62
Formalizing Glue KnowledgeDomain Map for
SYNAPSE and NCMIR

• Domain Map
• labeled graph with
• concepts ("classes") and
• roles ("associations")
• additional semantics expressed as logic rules
  (F-logic)

63
Source Contextualization DM Refinement

• sources can register new concepts at the
  mediator ...

64
ExampleANATOM Domain Map
65
Browsing Registered Data with Domain Maps
66
Process Maps with Abstractions and Elaborations
From Terminological to Procedural Glue
67
Summary Mediation Scenarios Techniques
Common Schema Mediated
Schema Common Glue Maps
SQL, rules XML
query languages DOOD query
languages Schema Transformations
Syntax-Aware Mappings Semantics-Aware
Mappings Syntactic Joins
Syntactic Joins Semantic Joins via
Glue Maps DB expert DB expert
KRDB domain expert
68
Semantic (Community) Webs
69
Combine EverythingDie eierlegende Wollmilchsau

• Database Federation/Mediation
• query rewriting under GAV/LAV
• w/ binding pattern constraints
• distributed query processing
• Semantic Mediation
• semantic integrity constraints, reasoning w/
  plans, automated deduction
• deductive database/logic programming technology,
  AI stuff...
• Semantic Web technology
• Scientific Workflow Management
• more procedural than database mediation (often
  the scientist is the query planner)
• deployment using web services

70
B R E A K

• ... followed by demos ...

71
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72
GEON SMART Metadata Multihierarchical Rock
Classification for Thematic Queries (GSC)
Genesis
Fabric
Composition
Texture
73
GEON SMART MetadataMultihierarchical Rock
Classification for Thematic Queries
http//klin-pc.sdsc.edu8080/examples/jsp/geon/co
mposition.jsp
74
GEON Ontology Demo

• http//klin-pc.sdsc.edu8080/examples/jsp/geon/old
  -rock.jsp
• http//klin-pc.sdsc.edu8080/examples/jsp/geon/roc
  k.jsp

75
Architecture of Ontology Based Map Integration
Ontology Mapping
Global Web Map Server
Web Map Server
Web Map Server
Web Map Server
Database
Database
Database
76
DOE Scientific Datamanagement Center

• Scientific Workflow Demo

77
Example A Scientific Workflow
Microarray analysis
Database search for promoter identification
cDNA Cluster
Promoter sequences
Promoter model
Common promoter alignment


- New candidate target genes



Database search
Adapted from Thomas Werner Biomolecular
Engineering, 17 87-94 (2001)
78
Conceptual Workflow
Compute clusters (min. distance)
For each promoter
Select gene-set (cluster-level)
Compute Subsequence labels
For each gene
With all Promoter Models
Compute Joint Promoter Model
79
Mapping This Workflow To Web Sites
80
Customized CGI Application
81
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82
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83
ClustalW Output
Transfac Query Results
84
SDM-SciDAC System Architecture
AWF
EWF
web service invocation
web service invocation
ET
ET
query rewriting
semantic type checking
data type conversion
web service matching
Genbank
BLAST
Abstract Task (AT) Repository
Data Parameter Ontologies
Datatype Conversion Repository
Executable Task (ET) Repository
85
AWF to EWF
User supplied
Declarative specification
GetGenomicSequence (selectedGene,
-GenomicSequence) - GENBANK
(selectedGene, -cDNASequence), BLAST
(cDNASequence, dbName, format,
-rankedGenomicSequenceList). GetGenomicSequence
(selectedGene, -GenomicSequence)
- GENBANK (selectedGene, -cDNASequence), BLA
T (cDNASequence, QueryType, SortCriteria,
OutputType , -rankedGenomicSequenceList). Ident
ifyPromoterElements (rankedGenomicSequenceList,
-element) - PromoterSequences
(rankedGenomicSequenceList, getBeginEnd(Specie
s, -Begin, -End), -element).
For each gene
Need extra domain knowledge
Translation to EWF needs creation of iterators
Same functionality, different operational
constraints and availability
86
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87
Abstract Task (AT) Registration
88
Abstract Task (AT) View and Delete
89
Abstract Task (AT) Update
90
AWF Design
91
EWF Planning and Compilation
92
EWF Execution
93
BIRN Tools Demo
94
Some References (starting points)

• XML
• General http//xml.coverpages.org/xml.html
• XQuery http//www.w3.org/XML/Query
• XSLT http//xml.coverpages.org/xsl.html
• Query Rewriting
• database research literature
• Logic Programming
• Learn Prolog Now! http//www.coli.uni-sb.de/kris
  /learn-prolog-now/
• SWI-Prolog (nice free Prolog system)
  http//www.swi-prolog.org/
• Ontologies
• Ontology Web language http//www.w3.org/TR/owl-fe
  atures/
• http//www-ksl.stanford.edu/kst/what-is-an-ontolog
  y.html
• http//www.cs.utexas.edu/users/mfkb/related.html
• Model-Based Mediation
• http//www.sdsc.edu/ludaesch/Paper/icde01.html
• Semantic Web
• http//www.w3.org/2001/sw/

95
References Project Web Sites

• GEOsciences Network (NSF)
• www.geongrid.org
• Biomedical Informatics Research Network (NIH)
• www.nbirn.net
• Science Environment for Ecological Knowledge
  (NSF)
• seek.ecoinformatics.org
• Scientific Data Management Center (DOE)
• sdm.lbl.gov/sdmcenter/