Transcript Department of Computer Science & Engineering University of California, San Diego CSE-291: Ontologies in Data Integration Spring 2003 Bertram Ludäscher [email protected] CSE-291: Ontologies in Data Integration.

Department of Computer Science & Engineering
University of California, San Diego
CSE-291: Ontologies in Data Integration
Spring 2003
Bertram Ludäscher
[email protected]
CSE-291: Ontologies in Data Integration
Course Overview
• Introduction to ontologies:
– What are ontologies (and some related “beasts”)?
– How do we represent ontologies?
– What can we do with them/to them?
• Introduction to some specific formalisms
– Logic, Description Logics, OWL, FCA, TMs, ...
• Some guest lectures
• “Class Action”:
– Theoretical studies:
• surveying/comparing/analyzing approaches and concrete ontologies
(based on research literature)
– Practical studies:
• applying an ontology/KR tool and methodology to a concrete domain
CSE-291: Ontologies in Data Integration
Acknowledgements and Credits
• 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/
• Last not least (background – and foreground material ;-)
Carole Goble, Nigel Shadbolt [Ontologies and the Grid Tutorial], Robert
Stevens, Ian Horrocks [Fact] , Alexander Maedche, Steffen Staab [ISWC
Tutorial], Stefan Decker, Nicola Guarino, John Sowa, ...
CSE-291: Ontologies in Data Integration
Information Integration, Ontologies, and
Scientific Data
• Some “e-Science” / “cyberinfrastructure” projects: applying IT in
difference scientific domains:
• Often: Share, interoperate, mediate, integrate data to...
– ... support scientific data and information management
– ... facilitate knowledge discovery
CSE-291: Ontologies in Data Integration
Data / Information Integration
CSE-291: Ontologies in Data Integration
An Online Shopper’s Information Integration Problem
El Cheapo: “Where can I get the cheapest copy (including shipping cost) of
Wittgenstein’s Tractatus Logicus-Philosophicus within a week?”
addall.com
?
Information
Integration
amazon.com
barnes&noble.com
half.com
“One-World”
Mediation
A1books.com
A Home Buyer’s 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?
?
Information
Integration
Realtor
Crime Stats
School Rankings
“Multiple-Worlds”
Mediation
Demographics
A Geoscientist’s 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?
?
Information
Integration
Geologic Map
(Virginia)
GeoChemical
“Complex
Multiple-Worlds”
Mediation
GeoPhysical GeoChronologic
(gravity contours) (Concordia)
Foliation Map
(structure DB)
A Neuroscientist’s 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?
?
Information
Integration
protein localization
sequence info
(NCMIR)
(CaPROT)
“Complex
Multiple-Worlds”
Mediation
morphometry
neurotransmission
(SYNAPSE)
(SENSELAB)
Standard (XML-Based) Mediator Architecture
USER/Client
Query Q ( G (S1,..., Sk) )
Integrated Global
(XML) View G
Integrated View
Definition
MEDIATOR
G(..) S1(..)…Sk(..)
(XML) Queries & Results
(XML) View
(XML) View
(XML) View
Wrapper
Wrapper
Wrapper
S1
S2
Sk
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wrappers implemented
as web services
Some BIRNing Data
Integration Questions
Biomedical Informatics
Research Network
http://nbirn.net
• Data Integration Approaches:
–
–
–
–
Let’s 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?”
CSE-291: Ontologies in Data Integration
Information Integration Challenges
• System aspects: “Grid” Middleware
Semantics
Structure
Syntax
System aspects
 reconciling S4
heterogeneities
 “gluing” together
multiple data sources
 bridging information
and knowledge gaps
computationally
CSE-291: Ontologies in Data Integration
– 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),
DAML+OIL, OWL ...)
– sources = knowledge bases (DB+CMs+ICs)
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)
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What’s 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? => 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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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 are useful!”
CSE-291: Ontologies in Data Integration
XML-Based vs. Model-Based Mediation
CM ~ {Descr.Logic, ER, UML, RDF/XML(-Schema), …}
Integrated-DTD :=
Glue Maps
XML-QL(Src1-DTD,...)
DMs, PMs
CM-QL ~ {F-Logic, DAML+OIL, …}
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
What is an ontology and
what is it good for?
And the answer is ...
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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]
• Some aspects and parameters:
– a formal specification (reasoning and “execution”)
– ... of a conceptualization of a domain (community)
– ... of some part of world that is of interest (application)
• 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?
CSE-291: Ontologies in Data Integration
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
ontology
conceptualization C(L)
CSE-291: Ontologies in Data Integration
[Guarino96]
http://www-ksl.stanford.edu/KR96/Guarino-What/P003.html
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
CSE-291: Ontologies in Data Integration
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?
CSE-291: Ontologies in Data Integration
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
CSE-291: Ontologies in Data Integration
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.
CSE-291: Ontologies in Data Integration
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
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
CSE-291: Ontologies in Data Integration
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
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]
CSE-291: Ontologies in Data Integration
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
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
Things
Meaning
Triangle
An explicit description of a domain
animal
• Concepts (class, set, type, predicate)
– event, gene, gammaBurst, atrium,
molecule, cat
• Properties of concepts and
relationships between them (slot)
vermin
domestic
cat
– Taxonomy: generalisation ordering
rodent eats
among concepts isA, partOf,
subProcess
mouse
– Relationship, Role or Attribute:
functionOf, hasActivity location, eats,
size
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dog
cow
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
Concepts
• Primitive concepts:
– properties are necessary
– Globular protein must have hydrophobic core (but a protein with
a hydrophobic core need not be a globular protein)
• Defined concepts:
– properties are necessary + sufficient
– Eukaryotic cells must have a nucleus.
– Every cell that contains a nucleus must be Eukaryotic.
[Robert Stevens]
CSE-291: Ontologies in Data Integration
What is a concept?
Different communities have different notions on what a concept
means:
– Formal concept analysis (see http://www.math.tudresden.de/~ganter/fba.html) talk about formal concepts
– Description Logics (see http://dl.kr.org/): They talk about
concept labels
– ISO-704:2000 – Terminology Work: (see http://www.iso.ch/)
– Often the classical notion of a frame in AI or a class in OO
modeling is seen as equivalent to a concept.
CSE-291: Ontologies in Data Integration
Formal Concept Analysis (FCA)
Formal Concept Analysis
[Sowa, http://users.bestweb.net/~sowa/misc/mathw.htm]
Concept Lattice
CSE-291: Ontologies in Data Integration
An explicit description of a domain
• Constraints or axioms on properties and concepts:
–
–
–
–
–
–
–
–
value: integer
domain: cat
cardinality: at most 1
range: 0 <= X <= 100
oligonucleiotides < 20 base pairs
cows are larger than dogs
cats cannot eat only vegetation
cats and dogs are disjoint
• Values or concrete domains
– integer, strings
– 20, trypotoplan-synthetase
CSE-291: Ontologies in Data Integration
animal
vermin
domestic
cat
rodent eats
dog
cow
mouse
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
An explicit description of a domain
animal
• Individuals or Instances
– sulphur, trpA Gene, felix
• Nominals
– Concepts that cannot have instances
– Instances that are used in conceptual definitions
– ItalianDog = Dog bornIn Italy
• Instances
– An ontology =
concepts+properties+axioms+values+nominals
– A knowledge base = ontology+instances
vermin
domestic
cat
rodent eats
mouse
dog
cow
felix
tom
mickey
jerry
CSE-291: Ontologies in Data Integration
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
Light and Heavy expressivity
A matter of rigour and representational expressivity
• Lightweight
• Heavyweight
– Concepts, atomic types
– Is-a hierarchy
– Relationships between
concepts
CSE-291: Ontologies in Data Integration
–
–
–
–
–
–
–
–
–
Metaclasses
Type constraints on relations
Cardinality constraints
Taxonomy of relations
Reified statements
Axioms
Semantic entailments
Expressiveness
Inference systems
[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]
[Mike Uschold, Boeing Corp]
A semantic continuum
Shared
human
consensus
Implicit
Pump: “a device for
moving a gas or liquid
from one place or
container to another”
(pump has
(superclasses (…))
Semantics
Text descriptions hardwired;
used at runtime
Semantics
processed and
used at runtime
Informal
Formal
(explicit)
Formal
(for humans)
(for machines)
Further to the right means:
•Less ambiguity
•More likely to have correct
functionality
•Better inter-operation (hopefully)
CSE-291: Ontologies in Data Integration
•Less hardwiring
•More robust to change
•More difficult
Some Ontologies (and Friends) in
Action
(coming soon to a project near you)
CSE-291: Ontologies in Data Integration
GEON Architecture
Midatlantic Region
Rocky Mountains
CSE-291: Ontologies in Data Integration
SMART (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.)
CSE-291: Ontologies in Data Integration
SMART Metadata II: Multihierarchical Rock Classification for “Thematic
Queries” (GSC) –– or: Taxonomies are not only for biologists ...
Genesis
Fabric
Composition
“smart discovery & querying” via
multiple, independent concept
hierarchies (controlled vocabularies)
• data at different description levels
can be found and processed
Texture
CSE-291: Ontologies in Data Integration
SMART Metadata III: Source
Contextualization & Ontology Refinement
Biomedical
Informatics
Research Network
http://nbirn.net
Focused GEON ontology working meeting
last week ... (GEON, SCEC/KR, GSC, ESRI)
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EcoCyc
CSE-291: Ontologies in Data Integration
Gene Ontology






CSE-291: Ontologies in Data Integration
http://www.geneontology.org
“a dynamic controlled vocabulary that
can be applied to all eukaryotes”
Built by the community for the
community.
Three organising principles:
 Molecular function, Biological
process, Cellular component
Isa and Part of taxonomy – but not
good!
~10,000 concepts
Lightweight ontology, Poor semantic
rigour. Ok when small and used for
annotation. Obstacle when large,
evolving and used for mining.
Controlled vocabulary
• AGROVOC: Agricultural Vocabulary
CSE-291: Ontologies in Data Integration
Thesauri
• AAT: Art & Architecture Thesaurus
CSE-291: Ontologies in Data Integration
One Formalism:
Description Logics
(aka terminological logics,
member of concept languages)
CSE-291: Ontologies in Data Integration
Formalism for Ontologies: Description Logic
• DL definition of “Happy Father”
(Example from Ian Horrocks, U Manchester, UK)
CSE-291: Ontologies in Data Integration
Description Logic Statements as Rules
• Another syntax: first-order logic in 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).
• Note:
– the direction “” is implicit here (*sigh*)
– see, e.g., Clark’s completion in Logic Programming
CSE-291: Ontologies in Data Integration
Description Logics
• Terminological Knowledge (TBox)
– Concept Definition (naming of concepts):
– Axiom (constraining of concepts):
=> a mediators “glue knowledge source”
• Assertional Knowledge (ABox)
– the marked neuron in image 27
=> the concrete instances/individuals of the concepts/classes that
your sources export
CSE-291: Ontologies in Data Integration
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) }
=> check happyFathers in a given database
• Reasoning:
– check if I |=  implies I |=  for all databases I,
– i.e., if  => 
– undecidable for FO, F-logic, etc.
– Descriptions Logics are decidable fragments
 concept subsumption, concept hierarchy, classification
 semantic tableaux, resolution, specialized algorithms
CSE-291: Ontologies in Data Integration
Formalizing Glue Knowledge:
Domain Map for SYNAPSE and NCMIR
Domain Map
= labeled graph with
concepts ("classes") and
roles ("associations")
• additional semantics: expressed
as logic rules
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
Domain Map (DM)
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DM in Description Logic
Source Contextualization & DM 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 ...
CSE-291: Ontologies in Data Integration