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Ontology Mapping andAlignment
Natasha NoyStanford University
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Outline
Semantic integration
Types of ontology mismatches
Mapping discovery
Using mappingsChallenges/Issues
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Lots of Overlapping Ontologieson the Semantic Web
Search Swoogle for
publication
185 matches in therepository
Different definitions,
viewpoints, notions
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"Basically, we're all trying to say the same thing."
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Semantic Integration Tasks
Queries across multiple resources
Data transformation
Reasoning with mappings
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Outline
Semantic Integration components andtasks
Types of ontology mismatches
Mapping discovery
Using mappings
Challenges/Issues
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Reasons for Mismatches
Ontology is not a reality it is a
subjective representation of itDifferent designers have different views
Different tasks and requirements for
applicationsDifferent conventions, etc.
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Types of Mismatches
Language-level mismatches
Difference in expressiveness or semantics ofontology language
Ontology-level mismatches
Difference in the structure of semantics of theontology
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Language-level Mismatches
Syntax
Expressivenesse.g., presence of disjoints, negations, expressions,unions, intersections, metaclasses, etc. in the language
Semantics of primitives
e.g., union vs intersection semantics for multiple domainand range declarations
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Ontology-level Mismatches
The same terms describing different concepts
Different terms describing the same concept
Different modeling paradigmse.g., intervals or points to describe temporal aspects
Different modeling conventions
Different levels of granularityDifferent coverage
Different points of view
...
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Ontology-level Mismatches:
Examples
Compare ontologieshttp://www.aktors.org/ontology/: the ontologyused in CS AKTive Portal testbed
http://ebiquity.umbc.edu/ontology/: the
ontology developed by the UMBC eBiquity group
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Some of the Differences
Different names for the same
conceptPhD-Student PhDStudent
Same term for different
conceptsProject: Only current
projects
Project: Past projects andproposals
Scope Includes periodicals,composite publications... Includes alumni, guestspeakers, etc
Different focus in definitionPublication:
Restrictions on citations, refs.
Publication: Restrictions
on abstract, editor
Constructs used Includes defined classes Only primitive classes
Different modeling conventins Journal is a class journal is a property
GranularityProfessor-In-
Academia
Adjunct, affiliated, associate,
principal, etc.
Different modeling
conventions and level of detail
address property broken
up into several properties
address property is a
single string property
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Outline
Semantic Integration components andtasks
Types of ontology mismatches
Mapping discovery
Using mappings
Challenges/Issues
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Categories of Mappings
Ontology-to-ontologymapping
Data matching
OntologyA
OntologyB
X Y?
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Mapping Discovery
Information Sources
Methods
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Mapping Discovery:Information Sources
A common reference ontology
Lexical informationOntology structure
User input
External resources
Prior matches
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Using a Common Reference
Ontolo
Upper ontologies designed to
support information integration
designed in principled way
provide common referenceterminology
SUMO, DOLCE
Domain-specific interlingua
PSL
OntologyA
OntologyB
Commonreferenceontology
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Solve the problem before it arises
Modes of mappingOntology-to-interlingua
Ontology-to-ontology, using the interlingua
information
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Using reference ontologies:Problems
People are reluctant to reuse
There have been some successes (in domain-specific
settings) and failures
Usual problems with having standards
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Using Lexical Information
String normalization
upper and lower case
blanks and delimitersdiacritics
stop-words
String distance
Hamming distance
edit distance
Soundex
Thesaurus
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Using Ontology Structure
IF-Map (Kalfoglou, Schorlemmer)
Using metrics to compare OWL concepts (Euzenat and
Volchev)QOM (Ehrig and Staab)
Similarity Flooding (Melnik, Garcia-Molina, Rahm)
Chimaera (Stanford KSL)
Prompt and AnchorPrompt (Stanford SMI)
a number of others...
QOM (Ehrig and Staab)
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Using External Sources
Grounding sources in standard
terminologies
WordNet
UMLSS-Match (U. Trento)
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User Input
Providing information on initial
alignmentProviding feedback on alignments
Invoking or composing alignment
methodsPrompt, Chimaera, ONION
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Using Prior Matches
Composing existing matchesSemantic gossiping
Using a corpus of matches
Alon Halevy (UW)
?
?
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Mapping Composition
?
Issues
Quality of initial mappings
Composition without loss of information
Choosing which composition route to follow
Semantic gossiping, Piazza (UW)
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Using Corpus of Matches
Contents of the corpus
Domain models
Instance data
Validated mappings
Queries
Meta-data
Statistics on the corpus
Term usageCo-occurrence of schema and ontology
O.Etzioni, A. Halevy, et.al. (UW)
?
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Mapping Methods
Heuristic and Rule-based methods
Graph analysis
Machine-learning
Probabilistic approachesReasoning, theorem proving
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Rule-Based and Graph-AnalysisMethods
Rule-based methodsMost structure-analysis and lexical analysis
methods
Prompt, Chimaera, QOM, IF-Map...
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Graph-based Methods
Treat ontologies as graphs andcompare the corresponding subgraphs
Similarity Flooding, Anchor-Prompt
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AnchorPrompt:Analyzing Graph Structure
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Machine Learning Approaches
Statistics of data contentUsing multiple learners
Using instance and values information
GLUE, LSD, SemInt
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Combining results produced byheuristic-based mappings
OMEN (Mitra & Noy)
m(C3, C
3')
m(C1, C1')
m(C2, C2')
Probabilistic Approaches
C3
C1
C1'
C2'C
2
C3'
More in data matching
R i d
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We are mostly an AI crowd, after all...
S-MatchStart with a combination of matchers using lexical
information and external resources
Use a SAT solver to find equivalence, generalization,and specialization mappings
Reasoning andTheorem Proving
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Outline
Semantic Integration components andtasks
Types of ontology mismatches
Mapping discovery
Using mappings
Challenges/Issues
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Using Mappings
Data transformation
Query answering
Reasoning with mappings
mapping composition (covered earlier)
Generation of ontology extensions
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Data Transformation
Mapping Interpreter (Stanford SMI),OntoMerge
m
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Data Transformation (II)
Mapping interpreter (Stanford SMI)
Uses an instantiated ontology of mappings
mapping structure
Python rules
OntoMerge
Treats source ontologies with data and mappingaxioms as a single ontology
Uses a theorem prover to create new data
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Query Answering
Two settings
one-to-one mappingsglobal ontology
m
Q
Q
Q
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Query Answering (II)
Piazza (UW)
Peer-to-peer architecture for queryanswering
Query refomulation using mappings betweenadjacent peers
Q
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Query Answering
OIS (Calvanese, et. al.)
Global ontology mapped to local ontologies
Mappings defined as views
Using a Description Logic Reasoner to answerqueries
Q
G ti f O t l
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Generation of OntologyExtensions
OntoMerge
OWL-S WSDLm
TripPlanner
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Outline
Semantic Integration components andtasks
Types of ontology mismatches
Mapping
Using mappings
Challenges/Issues
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Challenges/Issues
Design space of mapping approaches
Can we create a toolbox for designing mapping
approaches that fit a given problem?We have identified some components, but how can we
bring them together?
Have we reached a ceiling in mapping
discovery?Will it be lots of work for little gain from now on?
Are there serious untapped resources?
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Challenges/Issues
Are imperfect and inconsistent mappings
useful?
How do we maintain mappings whenontologies evolve?
How do we evaluate and compare different
tools?EON experiment
NIST experiment