Introduction to Databases:
From Data to Knowledge Bases
Instructors:
Bertram Ludaescher
Kai Lin
Overview
•
•
•
•
•
08:30-9:30
9:30 – 9:45
9:45 -11:20
11:20-11:50
11:50-13:15
Introduction to KR (1h)
BREAK (15’)
Intro to KR (1h45’)
Demos (30’)
LUNCH (1h25’)
• Demonstrations/Hands-on (~30’)
• Ontology-enabled data integration
• Concept map creation tool
• Ontology creation tool
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The Problem: Scientific Data Integration
or: … from Questions to Queries …
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Ontology Cheat Sheet (1/2)
• What is an ontology? An ontology usually …
– specifies a theory (a set of logic models) by …
– defining and relating …
– concepts representing features of a domain of interest
• Also overloaded (sloppy) for:
–
–
–
–
–
–
Controlled vocabularies
Database schema (relational, XML Schema/DTD, …)
Conceptual schema (ER, UML, … )
Thesauri (synonyms, broader term/narrower term)
Taxonomies (classifications)
Informal/semi-formal knowledge representations
• “Concept spaces”, “concept maps”
• Labeled graphs / semantic networks (RDF)
– Formal ontologies, e.g., in [Description] Logic (OWL)
• “formalization of a specification”
 constrains possible interpretation of terms
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Ontology Cheat Sheet (2/2)
• What are ontologies used for?
– Conceptual models of a domain or application,
(communication means, system design, …)
– Classification of …
• concepts (taxonomy) and
• data/object instances through classes
– Analysis of ontologies e.g.
• Graph queries (reachability, path queries, …)
• Reasoning (concept subsumption, consistency checking, …)
– Targets for semantic data registration
– Conceptual indexes and views for
•
•
•
•
searching,
browsing,
querying, and
integration of registered data
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Ontologies as Metadata++
Ontologies = Smarter Metadata
TM
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Smarter (Meta)data I: Logical Data Views
Adoption of a standard (meta)data
model => wrap data sets into
unified virtual views
Source: NADAM Team
(Boyan Brodaric et al.)
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Smarter Metadata II: Multihierarchical Rock Classification for “Thematic
Queries” (GSC) –– or: Taxonomies are not only for biologists ...
Genesis
Fabric
“smart discovery & querying” via
multiple, independent concept
hierarchies (controlled vocabularies)
• data at different description levels
can be found and processed
Composition
Texture
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Smarter Metadata III: Source
Contextualization & Ontology Refinement
Biomedical
Informatics
Research Network
http://nbirn.net
The next frontier:
Capturing Knowledge about Dynamic Processes
 “Process Ontologies”
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Ontology-Enabled Application Example:
Geologic Map Integration
domain
knowledge
Show
formations
where AGE =
‘Paleozic’
(without age
ontology)
(with age
ontology)
+/- a few hundred
million years
Nevada
Introduction to KR, B. Ludaescher & K. Lin
Show
formations
where AGE
= ‘Paleozic’
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Integrated querying of multiple datasets via
different “ontologies” (conceptual views)
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Querying by Geologic Age …
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Querying by Geologic Age: Result
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Querying by Chemical Composition …
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Querying by Chemical Composition: Results
Note the fine
differences in
shades of gray:
DO know:
It’s NOT there!
!
?
DON’T know!
(not registered)
OK – we got to work on the color coding ;-)
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Querying w/ British Rock Classification
Uses a GSC  BRC inter-ontology articulation mapping
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British Rock Classification Query: Results
Uses a GSC  BRC inter-ontology articulation mapping
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Different views on State Geological Maps
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The Query: Show sedimentary rocks
The Puzzle: Find the 17 differences in the results…
but first: what states are we looking at?
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Sedimentary Rocks: BGS Ontology
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Sedimentary Rocks: GSC Ontology
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Differing Conceptual Views: Why?
• We are looking at the same datasets – why do they
look different?
– Different rock classifications (GSC, BGS) are used as
“targets” for registering data to
– Not every rock name/rock type found in the raw data is
found in both classifications
– The mapping (“articulation”) between the classifications
is an approximation only
• Yet: having “conceptual views” (even if different)
on the data really seems like a good idea…
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Geologic Map Integration
• Given:
– Geologic maps from different state geological
surveys (shapefiles w/ different data schemas)
– Different ontologies:
• Geologic age ontology
• Rock classification ontologies:
– Multiple hierarchies (chemical, fabric, texture, genesis) from
Geological Survey of Canada (GSC)
– Single hierarchy from British Geological Survey (BGS)
• Problem:
– Support uniform queries using different ontologies
– Support registration w/ ontology A, querying w/
ontology B
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A Multi-Hierarchical Rock Classification
“Ontology” (really:Taxonomy)
Genesis
Fabric
Composition
Texture
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Implementation in OWL: Not only “for the machine” …
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Demonstration of
Ontology-enabled Map Integration (OMI) v2
Data
Data
Ontology enabled
Map Integrator {A,B}
ontology
A
ontology
B
Data
Application (B)
ontology
C
Application (C)
Data
Data sets
Introduction to KR, B. Ludaescher & K. Lin
Ontologies
26
Applications
Ontology Mapping: Overview
• Align ontologies
• Integrate data sets which are registered to different
ontologies
• Query data sets through different ontologies
register
Data set 1
Ontology 1
queries
Ontology mappings
Data set 2
Introduction to KR, B. Ludaescher & K. Lin
register
Ontology 2
27
Geology Workbench: Initial State
click on Ontologies
click on Datasets
click on Applications
An Ontology-based Mediator
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Geology Workbench: Uploading
Ontologies
click
on
Ontology
Submission
Choose
Click
antoOWL
checkfile
its to
detail
upload
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Name Space
Can be used to import this
ontology into others
Geology Workbench: Data (to Ontology!) Registration
Step 1: Choose Classes
Click on Submission
Data set name
Select a shapefile
Choose an ontology
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Geology Workbench: Data Registration
Step 2: Choose Columns for Selected Classes
It contains information about
geologic age
AREA
PERIMETER
AZ_1000
AZ_1000_I
D
GEO
PERIOD
ABBREV
DESCR
D_SYMBOL
P_SYMBOL
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Geology Workbench: Data Registration
Step 3: Resolve Mismatches
Two terms are
not matched any
ontology terms
Manually mapping
algonkian into
the ontology
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Geology Workbench: Ontology-enabled Map Integrator
All areas with the
age Paleozoic
Click on the name
Choose interesting
Classes
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Geology Workbench: Change Ontology
Run it
New query interface
Switch from Canadian
Rock Classification to
British Rock
Classification
Ontology mapping
between British Rock
Classification and Canadian
Rock Classification
Submit a mapping
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Ontology Repository
• Accept user-defined ontologies in OWL
• Any ontology saved in the system or accessible by can be imported
into another user-defined ontology ( inter-ontology references)
• Provide tool to browse the ontologies in the repository
composition.owl
……………..
<owl:Ontology>
<owl:imports rdf:resource=
"http://compute5.sdsc.geongrid.org:8080/workbench/jsp/ontologies/genesis.owl" />
</owl:Ontology>
…………….
<owl:Class rdf:ID="Ultramafite">
<rdfs:subClassOf rdf:resource="#Ultramafic"/>
<rdfs:subClassOf rdf:resource=
"http://compute5.sdsc.geongrid.org:8080/workbench/jsp/ontologies/genesis.owl#Igneous">
</owl:Class>
……………..
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Ontology-Enabled Map Integration:
Where do we stand?
The simple case (done) :
ontologies contain only the subclass relation
More complicate cases (coming soon) :
ontologies contain classes with attributes
ontologies with constraints in Description Logic
Implementation:
• v1,v2 prototypes: detail-level registration to ontology
• v3 (portal): item-level registration to ontology
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Current Ontology Registration (Item-level) v3
Domain Knowledge
Ontologies
Arizona
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GEON Search: Concept-based Querying
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System Overview
Client Access (via web services)
User Access (via Portal)
myOntology.owl
metadata
myDataset.foo
metadata
ResourceRegistration
GEON
Catalog
Other distributed apps
Kepler, DLESE, …
Search condition(s)
spatial temporal concept
GEONsearch
User actions
add delete manipulate
GEONworkbench
GEON
Workspace
(user)
SRB
Log
GEONmiddleware
external services
Gazetteer,
DLESE, …
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Geologic Age,
Chronos, …
Introduction to Knowledge
Representation and Ontologies
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Complex Multiple-Worlds Mediation and XML
• XML is Syntax
– DTDs talk about element nesting
– XML Schema schemas give you data types
– need anything else? => 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
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XML-Based vs. Model-Based Mediation
CM ~ {Descr.Logic, ER, UML, RDF/XML(-Schema), …}
Integrated-DTD :=
Ontologies
XQuery(Src1-DTD,...)
DMs, PMs
CM-QL ~ {F-Logic, OWL, …}
Integrated-CM :=
CM-QL(Src1-CM,...)
No Domain
Constraints
IF
 THEN 
IF
IFTHEN
THEN 
Structural Constraints (DTDs),
Parent, Child, Sibling, ...
A = (B*|C),D
B = ...
C1
C2
....
XML
Elements
XML Models
Raw
Raw
Data
RawData
Data
C3
R
....
. . ....
....
Logical
Domain
Constraints
Classes,
Relations,
is-a,
has-a, ...
(XML)
Objects
Conceptual Models
Knowledge Representation:
Relating Theory to the World via Formal Models
Source: John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations
“All models are wrong, but some models are useful!”
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What is an ontology??
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Glossary (wordreference.com)
•
ontology noun
1 (Philosophy) the branch of metaphysics that deals with the nature of being
2 (Logic) the set of entities presupposed by a theory
•
taxonomy noun
1 a the branch of biology concerned with the classification of organisms into groups based on
similarities of structure, origin, etc.b the practice of arranging organisms in this way
2 the science or practice of classification [ETYMOLOGY: 19th Century: from French
taxonomie, from Greek taxis order + -nomy]
•
thesaurus noun
(plural: -ruses, -ri [-raı])
1 a book containing systematized lists of synonyms and related words
2 a dictionary of selected words or topics
3 (rare)
a treasury[ETYMOLOGY: 18th Century: from Latin, Greek: treasure]
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Glossary (wordreference.com)
•
concept noun
1 an idea, esp. an abstract idea
example: the concepts of biology
2 (Philosophy) a general idea or notion that corresponds to some class of entities and that
consists of the characteristic or essential features of the class
3 (Philosophy) a the conjunction of all the characteristic features of something b a
theoretical construct within some theory c a directly intuited object of thought d the
meaning of a predicate
4 [modifier] (of a product, esp. a car) created as an exercise to demonstrate the technical
skills and imagination of the designers, and not intended for mass production or
sale[ETYMOLOGY: 16th Century: from Latin conceptum something received or conceived,
from concipere to take in, conceive]
•
contingent adjective
1 [when postpositive, often foll by on or upon] dependent on events, conditions, etc., not
yet known; conditional
2 (Logic) (of a proposition) true under certain conditions, false under others; not necessary
3 (in systemic grammar) denoting contingency (sense 4)
4 (Metaphysics) (of some being) existing only as a matter of fact; not necessarily existing
5 happening by chance or without known cause; accidental
6 that may or may not happen; uncertain
•
glossary noun (plural: -ries); an alphabetical list of terms peculiar to a field of knowledge
with definitions or explanations. Sometimes called: gloss
[ETYMOLOGY: 14th Century: from Late Latin glossarium; see gloss2]
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1st Attempt: Ontologies in CS
• An ontology is ...
– an explicit specification of a conceptualization [Gruber93]
– a shared understanding of some domain of interest [Uschold,
Gruninger96]
• Different aspects:
– a formal specification (reasoning and “execution”)
– ... of a conceptualisation of a domain (community)
– ... of some part of the world of interest (application, science domain)
• Provides:
– A common vocabulary of terms
– Some specification of the meaning of the terms (semantics)
– A shared “understanding” for people and machines
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Ontology as a philosophical discipline
• Ontology as a philosophical discipline, which deals with the
nature and the organization of reality:
– Ontology as such is usually contrasted with Epistemology, which
deals with the nature and sources of our knowledge [a.k.a. Theory of
Knowledge]. Aristotle defined Ontology as the science of being as
such: unlike the special sciences, each of which investigates a class
of beings and their determinations, Ontology regards all the species
of being qua being and the attributes which belong to it qua being"
(Aristotle, Metaphysics, IV, 1).
• In this sense Ontology tries to answer to the question:
What is being? What exists?
– the nature of being, not an enumeration of “stuff” around us…
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Some different uses of the word “Ontology”
[Guarino’95]
1. Ontology as a philosophical discipline
2. Ontology as a an informal conceptual system
3. Ontology as a formal semantic account
4. Ontology as a specification of a “conceptualization”
5. Ontology as a representation of a conceptual system
via a logical theory
5.1 characterized by specific formal properties
5.2 characterized only by its specific purposes
6. Ontology as the vocabulary used by a logical theory
7. Ontology as a (meta-level) specification of a logical theory
http://ontology.ip.rm.cnr.it/Papers/KBKS95.pdf
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Ontologies vs Conceptualizations
• Given a logical language L ...
– ... a conceptualization is a set of models of L which
describes the admittable (intended) interpretations of
its non-logical symbols (the vocabulary)
– ... an ontology is a (possibly incomplete) axiomatization
of a conceptualization.
set of all models M(L)
logic
theories
(consistent sets of
sentences; closed under
logical consequence)
ontology
conceptualization C(L)
[Guarino96]
http://www-ksl.stanford.edu/KR96/Guarino-What/P003.html
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Ontologies vs Knowledge Bases
• An ontology is a particular KB, describing facts assumed to
be always true by a community of users:
– in virtue of the agreed-upon meaning of the vocabulary used
(analytical knowledge):
• black => not white
– ... whose truth does not descend from the meaning of the vocabulary
used (non-analytical, common knowledge)
• Rome is the capital of Italy
• An arbitrary KB may describe facts which are contingently
true, and relevant to a particular epistemic state:
– Mr Smith’s pathology is either cirrhosis or diabetes
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Formal Ontology [Guarino’96]
• Theory of formal distinctions
– among things
– among relations
• Basic tools
– Theory of parthood
• What counts as a part of a given entity? What properties does the part
relation have? Are the different kinds of parts?
– Theory of integrity
• What counts as a whole? In which sense are its parts connected?
– Theory of identity
• How can an entity change while keeping its identity? What are its
essential properties? Under which conditions does an entity loose its
identity? Does a change of “point of view” change the identity
conditions?
– Theory of dependence
• Can a given entity exist alone, or does it depend on other entities?
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Ontology: Definition and Scope [Sowa]
• The subject of ontology is the study of the categories of things that
exist or may exist in some domain. The product of such a study, called
an ontology, is a catalog of the types of things that are assumed to
exist in a domain of interest D from the perspective of a person who
uses a language L for the purpose of talking about D. The types in the
ontology represent the predicates, word senses, or concept and relation
types of the language L when used to discuss topics in the domain D.
• An uninterpreted logic, such as predicate calculus, conceptual graphs, or
KIF, is ontologically neutral. It imposes no constraints on the subject
matter or the way the subject may be characterized. By itself, logic
says nothing about anything, but the combination of logic with an
ontology provides a language that can express relationships about the
entities in the domain of interest.
http://users.bestweb.net/~sowa/ontology/index.htm
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Ontology: Definition and Scope [Sowa]
• An informal ontology may be specified by a catalog of types that are
either undefined or defined only by statements in a natural language. A
formal ontology is specified by a collection of names for concept and
relation types organized in a partial ordering by the type-subtype
relation. Formal ontologies are further distinguished by the way the
subtypes are distinguished from their supertypes:
– an axiomatized ontology distinguishes subtypes by axioms and definitions
stated in a formal language, such as logic or some computer-oriented
notation that can be translated to logic
– a prototype-based ontology distinguishes subtypes by a comparison with a
typical member or prototype for each subtype.
• Large ontologies often use a mixture of definitional methods: formal
axioms and definitions are used for the terms in mathematics, physics,
and engineering; and prototypes are used for plants, animals, and
common household items. .
http://users.bestweb.net/~sowa/ontology/index.htm
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Why develop an ontology?
• To make domain assumptions explicit
– Easier to change domain assumptions
– Easier to understand, update, and integrate legacy data
 data integration
• To separate domain knowledge from operational
knowledge
– Re-use domain and operational knowledge separately
• A community reference for applications
• To share a consistent understanding of what
information means.
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
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What is being shared?
Metadata
• Data describing the content and meaning of resources
and services.
• But everyone must speak the same language…
Terminologies
• Shared and common vocabularies
• For search engines, agents, curators, authors and users
• But everyone must mean the same thing…
Ontologies
• Shared and common understanding of a domain
• Essential for search, exchange and discovery
 Ontologies aim at sharing meaning
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
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Origin and History
• Humans require words (or at least symbols) to communicate
efficiently. The mapping of words to things is indirect. We do it by
creating concepts that refer to things.
• The relation between symbols and things has been described in the
form of the meaning triangle:
Concept
“Jaguar“
Ogden, C. K. & Richards, I. A. 1923. "The Meaning
of Meaning." 8th Ed. New York, Harcourt, Brace &
World, Inc
before: Frege, Peirce; see [Sowa 2000]
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
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Human and machine communication
[Maedche et al., 2002]
• ...
Human
Agent 1
Human
Agent 2
exchange symbol,
e.g. via nat. language
Machine
Agent 1
Machine
Agent 2
exchange symbol,
e.g. via protocols
Ontology
Description
Symbol
‘‘JAGUAR“
Formal Semantics
Internal
models
commit
commit
commit
Concept
MA1
HA2
HA1
Formal
models
Ontology
commit
a specific
domain, e.g.
animals
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MA2
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Things
Meaning
Triangle
Introduction to Description Logics
References:
• F. Baader, W. Nutt. Basic Description Logics. In the
Description Logic Handbook, edited by F. Baader, D.
Calvanese, D.L. McGuinness, D. Nardi, P.F. Patel-Schneider,
Cambridge University Press, 2002, pages 47-100.
• Description Logics Tutorial, Ian Horrocks and Ulrike
Sattler, ECAI-2002, Lyon, France, July 23rd, 2002.
• Emerging Sparrow toolkit (Bowers, Ludaescher)
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Example: Description Logic
• DL definition of “Happy Father”
(Example from Ian Horrocks, Ulrike Sattler, U Manchester)
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Science Example: Ontology for SYNAPSE and NCMIR
Purkinje cells and Pyramidal cells have dendrites
that have higher-order branches that contain spines.
Dendritic spines are ion (calcium) regulating components.
Spines have ion binding proteins. Neurotransmission
involves ionic activity (release). Ion-binding proteins
control ion activity (propagation) in a cell. Ion-regulating
components of cells affect ionic activity (release).
Domain Expert Knowledge
DM in Description Logic
Domain Map (DM)
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Source Contextualization, Ontology Refinement
In addition to registering
(“hanging off”) data relative to
existing concepts, a source
may also refine the mediator’s
domain map...
 sources can register new
concepts at the mediator ...
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Some Description Logics History
• “Structured Inheritance Networks” [Brachman
1977]
• KL-ONE [Brachman, Schmolze 1985]
• Core ideas:
– Building blocks: atomic concepts (unary predicates),
atomic roles (binary predicates), individuals (constants)
– Constructors for building complex concepts and roles
from simpler ones
– Automated inference for concept subsumption and
instance classification (is-a/is-instance-of are not
explicitly given by the user, but inferred from concept
definitions/instance properties)
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Source: Description Logics Tutorial, Ian Horrocks and
Ulrike Sattler, ECAI-2002, Lyon, France, July 23rd, 2002
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Knowledge Base (DL-Style)
• Terminological Knowledge (TBox)
– Concept Definition (naming of concepts):
– Axiom (constraining of concepts):
=> a mediators “glue knowledge source”
• Assertional Knowledge (ABox) about Individuals
– n27_img118 : Neuron
=> the concrete instances/individuals of the
concepts/classes that your sources export
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Example TBox
Atomic concepts = {P,F,W, M1,…}
Base concepts = {P,F}
Defined concepts = {W, M1, M2, …}
Roles = {h1,h2}
Concept Definition
Axiom
where A atomic concept,
C, D complex concept expressions
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Example TBox
• Base concepts = {Person, Female}
… occur on the RHS only
• Defined concepts = {P, F, W, …}
… occur on the LHS (& maybe RHS)
• Base interpretation J: interpret base
concepts only
• Extension I of J: on same domain as J
and agrees (on base) with J
• TBox T is definitorial if every base
interpretation has exactly one extension
that is a model of T
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Brains-On (Hands-off) Session
TM
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What do we mean here?
Starting with the base
interpretation of
• I(Person) := “the class of persons”
• I(Female) := “the class of
females”
… what is the meaning of the
defined concepts?
… what role play the roles in this
process?
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And the answer is …
•
•
•
•
•
atomic concept
atomic concept
concept def. w/ intersection
… plus negation
… existential restriction
• … value restriction
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Digression:
“Sparrow” (Prolog) Syntax for DL
Sparrow “Grammar” and
“Parser”
Example in
Sparrow Syntax
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Back to Reasoning with the Family ...
• concept definition: MyConcept  DL-formula
• concept inclusion: MyConcept  DL-formula
• finite set of definitions is a terminology or
TBox if for every atomic concept A there is at
most one axiom whose lhs is A
Introduction to KR, B. Ludaescher & K. Lin
72
Expansion of Terminologies
• For acyclic T we can “unfold” concept definitions until
every defined concepts is specified in terms of
primitive concepts only
 the expansion of a TBox T
• Example:
Introduction to KR, B. Ludaescher & K. Lin
73
Reasoning in the Tableaux calculus
From this
TBox
We want to show this
Expansion
In First-order (LeanTap) syntax
Introduction to KR, B. Ludaescher & K. Lin
74
Reasoning Services
• Remember the distinction between evaluation a
query (over a DB) vs reasoning with queries
(symbolic expressions)?
• The former can be very hard, esp. for large
databases and complex queries
• The latter is much harder still, even for small
queries and knowledge bases, ontologies
• Specialized DL reasoners (FACT, Racer, …) better
than general purpose FO reasoners
Introduction to KR, B. Ludaescher & K. Lin
75
OK – enough of that jazz…
let’s look at some demos …
Introduction to KR, B. Ludaescher & K. Lin
76
Tools for Editing and Processing Ontology
1. Protégé 2000 (RDF, OWL)
http://protege.stanford.edu/
2. CmapTools (concept map)
http://cmap.ihmc.us/
3. Java API
Jena http://www.hpl.hp.com/semweb/jena.htm
OWL API http://sourceforge.net/projects/owlapi
Geology Map Integration Demo:
http://geon01.sdsc.edu:8080/workbench/jsp/onto-list.jsp
Introduction to KR, B. Ludaescher & K. Lin
77
ANOTHER APPLICATION OF ONTOLOGIES:
An Ontology-Driven Framework for Data
Transformation in Scientific Workflows
(from DILS’04)
Shawn Bowers
Bertram Ludäscher
San Diego Supercomputer Center
University of California, San Diego
Introduction to KR, B. Ludaescher & K. Lin
78
Outline
• Background (SEEK Project)
• Scientific Workflows
• The Problem: Reusing Structurally
Incompatible Services
• The Ontology-Driven Framework
• Future Work
Introduction to KR, B. Ludaescher & K. Lin
79
Outline
• Background (SEEK Project)
• Scientific Workflows
• The Problem: Reusing Structurally
Incompatible Services
• The Ontology-Driven Framework
• Future Work
Introduction to KR, B. Ludaescher & K. Lin
80
Science Environment for
Ecological Knowledge (SEEK)
• Domain Science Driver
– Ecology (LTER),
biodiversity, …
• Analysis & Modeling
System
– Design and execution of
ecological models and
analysis
– End user focus
– {application,upper}ware
Architecture (cf. US cyberinfrastructure,
UK e-Science)
• Semantic Mediation
System
– Data Integration of
hard-to-relate sources
and processes
– Semantic Types and
Introduction to KR, B. Ludaescher & K. Lin
Ontologies
this paper
81
Outline
• The SEEK Project
• Scientific Workflows
– Focus: analysis & component integration on top of
data integration
• The Problem: Reusing Structurally
Incompatible Services
• The Ontology-Driven Framework
• Future Work
Introduction to KR, B. Ludaescher & K. Lin
82
Promoter Identification in Kepler
[SSDBM’03]
•
Problems
–
Many components (web
serivces) are NOT
designed to fit!
“The problem P that X solves
is simple, and X doesn’t
solve it well”
– Semantically
meaningful connections
are structurally
incompatible
•
Approach
–
–
–
–
Introduction to KR, B. Ludaescher & K. Lin
83
Distinguish structural
type and semantic type
Structural type: e.g.
XML Schema
Semantic type: e.g.
OWL expressions
Exploit the (optional!)
semantic type as much as
possible
Service Reusability
A scientist wishes to connect two
(independent) services
Source
Service
Introduction to KR, B. Ludaescher & K. Lin
Desired Connection
Pt
Ps
84
Target
Service
Service Reusability
In Ptolemy II/Kepler (and in web services), input and
output ports (message parts) have structural types
(XML Schema)
Structural
Type Ps
Source
Service
Introduction to KR, B. Ludaescher & K. Lin
Structural
Type Pt
Desired Connection
Pt
Ps
85
Target
Service
Service Reusability
Unless “designed to fit,” independent services
are structurally incompatible
 Generally, the source output type will not
be a subtype of the target input type
Structural
Type Ps
Source
Service
Introduction to KR, B. Ludaescher & K. Lin
Incompatible
(⋠)
Structural
Type Pt
Desired Connection
Pt
Ps
86
Target
Service
Service Reusability
A transformation mapping () is required to
connect the services … artificially creating
subtype compatibility
If such a  exists, the services are
“structurally feasible”
Structural
Type Ps
Incompatible
(⋠)

Source
Service
Introduction to KR, B. Ludaescher & K. Lin
(Ps)
Structural
Type Pt
(≺)
Desired Connection
Pt
Ps
87
Target
Service
Service Reusability
We can annotate services with semantic
types for discovery and interoperability
of services
Ontologies (OWL)
Compatible (⊑)
Semantic
Type Ps
Source
Service
Introduction to KR, B. Ludaescher & K. Lin
Semantic
Type Pt
Desired Connection
Pt
Ps
88
Target
Service
Service Reusability
Services can be semantically compatible, but
structurally incompatible
Ontologies (OWL)
Compatible (⊑)
Semantic
Type Ps
Structural
Type Ps
Incompatible
(⋠)

Source
Service
Introduction to KR, B. Ludaescher & K. Lin
(Ps)
Semantic
Type Pt
Structural
Type Pt
(≺)
Desired Connection
Pt
Ps
89
Target
Service
Example Structural Types (XML)
structType(P2)
root
elem
elem
elem
elem
elem
structType(P3)
population
sample
meas
cnt
acc
lsp
=
=
=
=
=
=
(sample)*
(meas, lsp)
(cnt, acc)
xsd:integer
xsd:double
xsd:string
root
elem
elem
elem
<population>
<sample>
<meas>
<cnt>44,000</cnt>
<acc>0.95</acc>
</meas>
<lsp>Eggs</lsp>
</sample>
…
<population>
P3
S1
S2
(mortality rate
for period)
(life stage property)
P4
Introduction to KR, B. Ludaescher & K. Lin
(measurement)*
(phase, obs)
xsd:string
xsd:integer
<cohortTable>
<measurement>
<phase>Eggs</cnt>
<obs>44,000</acc>
</measurement>
…
<cohortTable>
P2
P1
cohortTable =
measuremnt =
phase
=
obs
=
90
P5
Example Semantic Types
Portion of SEEK measurement ontology
appliesTo
MeasContext
hasContext 0:*
MeasProperty
hasProperty
itemMeasured
Observation
0:*
1:*
Spatial
Location
hasLocation
1:1
Numeric
Value
hasCount
hasValue
Introduction to KR, B. Ludaescher & K. Lin
Entity
Ecological
Property
Accuracy
Qualifier
1:1
1:1
1:1
91
Abundance
Count
LifeStage
Property
Example Semantic Types
Portion of SEEK measurement ontology
appliesTo
MeasContext
Same in OWL, a description
logic0:*standard (here, Sparrow syntax):
hasContext
1:1
Observation subClassOf forall hasContext/MeasContext and
hasProperty
itemMeasured
forall
hasProperty/MeasProperty
and
MeasProperty
Observation
Entity
0:*
exists itemMeasured/Entity.
1:*
MeasContext subClassOf exists appliesTo/Entity and
atmost 1/appliesTo. Ecological
Property
Accuracy
EcologicalProperty subClassOf Entity.
Qualifier
LifeStageProperty subClassOf EcologicalProperty.
Spatial
LifeStage
hasLocation Abundance
AbundanceCount subClassOf
EcologicalProperty
and
Location
Count
Property
1:1
exists hasLocation/SpatialLocation and
hasValue
atMost 1/hasLocation and
1:1
Numeric
exists hasCount
hasCount/NumericValue and
ValueatMost
1:11/hasCount.
Introduction to KR, B. Ludaescher & K. Lin
92
Example Semantic Types
Semantic types for P2 and P3
MeasContext
Observation
hasContext
appliesTo
1:1
semType(P3)
1:1
itemMeasured Abundance
Count
1:1
LifeStage
Property
hasCount
Number
Value
1:1
1:1
⊑
hasProperty
semType(P2)
1:1
Accuracy
Qualifier
P2
P1
P3
S1
S2
(mortality rate
for period)
(life stage property)
P4
Introduction to KR, B. Ludaescher & K. Lin
hasValue
93
P5
Example Semantic Types
Semantic types for P2 and P3
MeasContext
semType(P3) subClassOf Observation and
Observation
exists hasContext/(MeasurementContext and
exists appliesTo/LifeStageProperty
and
hasContext
appliesTo
LifeStage
atMost 1/appliesTo) and
Property
1:1
1:1
exists itemMeasured/AbundanceCount
and
atMost 1/itemMeasured.
itemMeasured Abundance hasCount
Number
semType(P3)
Count
Value
1:1
1:1
semType(P2) subClassOf Observation and
1:1
⊑
exists hasContext/(MeasurementContext
and
hasValue
hasProperty
exists
appliesTo/LifeStageProperty and
Accuracy
semType(P2)
atMost
1/appliesTo) and
Qualifier
1:1
exists itemMeasured/AbundanceCount and
atMost 1/itemMeasured and
exists hasProperty/AccuracyQualifier
and
P2
P3
P5
P1
atMost 1/hasProperty.
S
2
S1
(mortality rate
for period)
(life stage property)
P4
Introduction to KR, B. Ludaescher & K. Lin
94
Outline
• The SEEK Project
• Scientific Workflows
• The Problem: Reusing Structurally
Incompatible Services
• The Ontology-Driven Framework
• Future Work
Introduction to KR, B. Ludaescher & K. Lin
95
The Ontology-Driven Framework
Define semantic registration mappings (“semantic
views”) to connect structural and semantic types
Use registration mappings to (semi-) automate
transformation, based on derived structural
correspondences
Depending on the ontologies and registration mappings, it
may not be possible to find an appropriate  …
(since the correspondence is often under-specified)
Introduction to KR, B. Ludaescher & K. Lin
96
The Ontology-Driven Framework
Ontologies (OWL)
Semantic
Type Ps
Compatible
(⊑)
Registration
Mapping (Input)
Registration
Mapping (Output)
Structural
Type Ps
Source
Service
Ps
Introduction to KR, B. Ludaescher & K. Lin
Semantic
Type Pt
Structural
Type Pt
Desired Connection
97
Pt
Target
Service
Registration Example (simple XPaths)
structType(P2)
root
elem
elem
elem
elem
elem
population
sample
meas
cnt
acc
lsp
=
=
=
=
=
=
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
Introduction to KR, B. Ludaescher & K. Lin
<population>
<sample>
<meas>
<cnt>44,000</cnt>
<acc>0.95</acc>
</meas>
<lsp>Eggs</lsp>
</sample>
…
<population>
(sample)*
(meas, lsp)
(cnt, acc)
xsd:integer
xsd:double
xsd:string
==
==
==
==
==
==
98
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
Registration Example (simple XPaths)
structType(P2)
root
elem
elem
elem
elem
elem
population
sample
meas
cnt
acc
lsp
=
=
=
=
=
=
<population>
<sample>
<meas>
<cnt>44,000</cnt>
<acc>0.95</acc>
</meas>
<lsp>Eggs</lsp>
</sample>
…
<population>
(sample)*
(meas, lsp)
(cnt, acc)
xsd:integer
xsd:double
xsd:string
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
Each sample is an instance of the semantic type
Introduction to KR, B. Ludaescher & K. Lin
99
Registration Example (simple XPaths)
structType(P2)
root
elem
elem
elem
elem
elem
population
sample
meas
cnt
acc
lsp
=
=
=
=
=
=
<population>
<sample>
<meas>
<cnt>44,000</cnt>
<acc>0.95</acc>
</meas>
<lsp>Eggs</lsp>
</sample>
…
<population>
(sample)*
(meas, lsp)
(cnt, acc)
xsd:integer
xsd:double
xsd:string
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
Each sample’s cnt represents the itemMeasured object
Introduction to KR, B. Ludaescher & K. Lin
100
Registration Example (simple XPaths)
structType(P2)
root
elem
elem
elem
elem
elem
population
sample
meas
cnt
acc
lsp
=
=
=
=
=
=
<population>
<sample>
<meas>
<cnt>44,000</cnt>
<acc>0.95</acc>
</meas>
<lsp>Eggs</lsp>
</sample>
…
<population>
(sample)*
(meas, lsp)
(cnt, acc)
xsd:integer
xsd:double
xsd:string
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
Each sample’s cnt’s value represents the hasCount value of
the corresponding itemMeasured object
Introduction to KR, B. Ludaescher & K. Lin
101
Registration Example (simple XPaths)
structType(P3)
root
elem
elem
elem
cohortTable =
measuremnt =
phase
=
obs
=
<cohortTable>
<measurement>
<phase>Eggs</cnt>
<obs>44,000</acc>
</measurement>
…
<cohortTable>
(measurement)*
(phase, obs)
xsd:string
xsd:integer
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
… similary for P3 .. … .
Introduction to KR, B. Ludaescher & K. Lin
102
The Ontology-Driven Framework
Ontologies (OWL)
Semantic
Type Ps
Compatible
Registration
Mapping (Input)
Registration
Mapping (Output)
Structural
Type Ps
Source
Service
(⊑)
Ps
Introduction to KR, B. Ludaescher & K. Lin
Correspondence
Desired Connection
103
Semantic
Type Pt
Structural
Type Pt
Pt
Target
Service
Correspondence Example
Source-side semantic registration mapping
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
Target-side semantic registration mapping
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
population
sample *
meas
cnt
xsd:integer
cohortTable
measurement *
obs
xsd:integer
phase
xsd:string
acc
xsd:double
lsp
xsd:string
Introduction to KR, B. Ludaescher & K. Lin
104
Correspondence Example
Source
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
Target
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
We want to “compose”
the registrations to obtain
cohortTable
structural correspondences
population
sample *
meas
cnt
xsd:integer
measurement *
obs
xsd:integer
phase
xsd:string
acc
xsd:double
lsp
xsd:string
Introduction to KR, B. Ludaescher & K. Lin
105
Correspondence Example
Source
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
Target
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
population
sample *
meas
cnt
xsd:integer
acc
xsd:double
lsp
xsd:string
Introduction to KR, B. Ludaescher & K. Lin
cohortTable
measurement *
obs
xsd:integer
phase
xsd:string
These fragments correspond
106
Correspondence Example
Source
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
Target
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
population
sample *
meas
cnt
xsd:integer
acc
xsd:double
lsp
xsd:string
Introduction to KR, B. Ludaescher & K. Lin
cohortTable
measurement *
obs
xsd:integer
phase
xsd:string
These fragments correspond
107
Correspondence Example
Source
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
Target
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
population
sample *
meas
cnt
xsd:integer
acc
xsd:double
lsp
xsd:string
Introduction to KR, B. Ludaescher & K. Lin
cohortTable
measurement *
obs
xsd:integer
phase
xsd:string
These fragments correspond
108
Correspondence Example
Source
/population/sample
/population/sample/meas/cnt
/population/sample/meas/cnt/text()
/population/sample/meas/acc
/population/sample/meas/acc/text()
/population/sample/lsp/text()
==
==
==
==
==
==
semType(P2)
semType(P2).itemMeasured
semType(P2).itemMeasured.hasCount
semType(P2).hasProperty
semType(P2).hasProperty.hasValue
semType(P2).hasContext.appliesTo
==
==
==
==
semType(P3)
semType(P3).itemMeasured
semType(P3).itemMeasured.hasCount
semType(P3).hasContext.appliesTo
Target
/cohortTable/measurement
/cohortTable/measurement/obs
/cohortTable/measurement/obs/text()
/cohortTable/measurement/phase/text()
population
sample *
meas
cnt
xsd:integer
acc
xsd:double
lsp
xsd:string
Introduction to KR, B. Ludaescher & K. Lin
cohortTable
measurement *
obs
xsd:integer
phase
xsd:string
These fragments correspond
109
The Ontology-Driven Framework
Ontologies (OWL)
Semantic
Type Ps
Compatible
Registration
Mapping (Input)
Registration
Mapping (Output)
Structural
Type Ps
Correspondence
Generate
Source
Service
(⊑)
Structural
Type Pt
(Ps)
Transformation
Ps
Introduction to KR, B. Ludaescher & K. Lin
Semantic
Type Pt
Desired Connection
110
Pt
Target
Service
Example Result (XQuery)
Based on the structural correspondences and
certain assumptions, we derive the
transformation XQuery:
<cohortTable>
{ for $s in /population/sample return
<measurement>
{ for $c in $s/meas/cnt return <obs>{$c/text()}</obs> }
{ for $l in $s/lsp return <phase>{$l/text()}</phase> }
</measurement>
}
</cohortTable>
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111
Assumptions Made
(or why this may not work for you…)
• Common XPath prefixes refer to the
same element
• Elements in correspondences have
compatible cardinalities
– source is equivalent or stricter than
target (e.g., + is stricter than *)
• Primitive data types are compatible
Introduction to KR, B. Ludaescher & K. Lin
112