Ontology Mapping and Alignment-2005

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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