Grass roots class alignment
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Grass-Roots Class Alignment. Baoshi Yan Information Sciences Institute, University of Southern California. Motivation. Sharing Structured Data among peers However, peers might use different terminology (Ontology). Need Ontology Alignment. What is Alignment.

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Grass-Roots Class Alignment

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Grass roots class alignment

Grass-Roots Class Alignment

Baoshi Yan

Information Sciences Institute,

University of Southern California


Motivation

Motivation

  • Sharing Structured Data among peers

  • However, peers might use different terminology (Ontology)

Need Ontology Alignment


What is alignment

What is Alignment

  • Correspondence between concepts


Alignment state of the art

Alignment: State of the Art

  • Heuristics-based:

    • Name similarity

    • Structure similarity

    • Instance

    • Constraints

    • Co-occurrence

  • Domain Expert

  • Centralized

  • Precise Alignment


Our approach

Our Approach

  • Cursory Alignment by End Users

    • Easy to produce

  • Combining different user’s alignments

    • Reuse to reduce effort by each user

  • Grass-Roots Alignment

Alignment Corpus

Peer-to-Peer Alignment


Grass roots class alignment

Grass-roots Alignment Example: WebScripter tool

when a user puts different stuffs

into the same column, they mean same thing

Inferred Alignment:

iswc:Person = isi: Div2Member

Inferred Alignment:

iswc:phone = isi: phonenumber


Properties of grass roots alignment

O2

O1

Graduate

GraduateStudent

PhDStudent

MSStudent

O3

O4

Doctoral

Student

Master

Student

Properties of Grass-Roots Alignment

  • Might be

    • Approximate

    • inconsistent

    • Intransitive


Challenge

Challenge

  • How to reuse approximate or inconsistent grass-roots alignments for alignment purposes

    • Approximation

      • conservative semantics of alignment

    • Inconsistency

      • evidences


Observations assumptions

A

A

O1

O2

O1

O2

A

O2

B

A

O2

B

B

B

C

C

C

C

(a)

(b)

O1

O2

O1

O2

A

A

C

A

C

A

B

C

B

B

B

C

(c)

(d)

Observations & Assumptions

  • Users tend to pick closest alignment candidate


Basic idea

Basic Idea:

  • Class relationships specified in ontology

    • definite

  • Class relationships indicated by previous alignments

    • Indefinite/ambiguous

  • Inference to get more Definite class relationships

  • Use these class relationships for future alignment


Class alignment algorithm step 1

Class Alignment Algorithm:Step 1

  • Subclass Relationships Specified in the Ontology


Class alignment algorithm step 2

C

A

A

,

,

NOT

NOT

O1

O2

A

A

B

B

C

C

B

A

A

B

C

C

B

OR

B

C

Class Alignment Algorithm:Step 2

  • Class Relationships Implied by Grass-roots Alignments: the Semantics of Grass-roots Alignments:


The semantics of grass roots alignments cont

the Semantics of Grass-roots Alignments (Cont)

O1

O2

A

A

C

B

NOT

C

B


The semantics of grass roots alignments cont1

O1

O2

A

D

A · D

B

C

B · C

the Semantics of Grass-roots Alignments (Cont)


Class alignment algorithm step 21

Class Alignment Algorithm:Step 2

  • Class Relationships Implied by Alignments


Class alignment algorithm step 3 forward chaining inference

Class Alignment Algorithm:Step 3: Forward-chaining Inference


Dealing with evidences

Dealing with Evidences

  • (f1, e1) AND (f2, e2) ... AND (fi, ei) = > (f, e), its evidence e = e1*e2*..*ei.

  • same fact supported by evidences e1, e2, ..ei, e = e1+e2+...+ei.

  • Also note that same evidence doesn't count twice, that is, e1 + e1 = e1, e1 * e1 = e1.

  • Quantifying Evidences:

    • V(e): a numerical value between (0, 1).

    • V(e1+e2) = 1-(1-V(e1))*(1-V(e2))

    • V(e1*e2) = V(e1)*V(e2)


Class alignment algorithm step 4 class alignment using facts kb

Class Alignment AlgorithmStep 4: Class Alignment Using Facts KB

  • Sup(A): the set of superclasses of A

  • Sub(A): the set of subclasses of A

  • Ind(A): all B such that

    • (A > B OR B > A)

    • neither A > B or B > A is in KB

    • I.e., B and A are indistinguishable according to facts KB.

  • deal with inconsistencies:

    • for each B from Sup(A), if there is a better-supported fact A > B, NOT(B > A) or B||A, remove B from Sup(A). Do the same to Sub(A).


Class alignment using facts kb cont

Class Alignment Using Facts KB (cont)

  • Examples:

  • Ind(MasterStudent)={MSStudent}

  • Sup(MasterStudent)={Graduate,Student,UnivStudent}

  • Sub(Graduate)={MasterStudent,MSStudent,DoctoralStudent}


Class alignment using facts kb cont1

Class Alignment Using Facts KB (cont)

  • Given A from O1, find best alignment B in O2 in the following order:

    • O2 ∩ Ind(A)

    • O2 ∩ Sup(A)

      • If B, B1 ∈ O2 ∩ Sup(A), pick B if B1 > B

    • O2 ∩ Sub(A)

      • If B, B1 ∈ O2 ∩ Sub(A), pick B if B > B1

  • Everything being equal, pick better supported

  • Otherwise no alignment candidate for A in O2.


Class alignment using facts kb cont2

Class Alignment Using Facts KB (cont)

  • Example:

    • Ind(MasterStudent)={MSStudent}

    • Sup(DoctoralStudent)={Graduate,Student,UnivStudent}

    • Ind(Student)={UnivStudent}

O1

O2

UnivStudent

Student

Graduate

DoctoralStudent

MasterStudent

MSStudent


Evaluation qualitative analysis

Evaluation (qualitative analysis)

  • In the ideal case:

    • Each previous alignment is best possible

    • Then: Guaranteed Correctness in some cases

O1

O2

UnivStudent

Student

Graduate

DoctoralStudent

  • Sup(DoctoralStudent)=

  • {UnivStudent,Graduate}

  • In the not-so-ideal case:

    • Bad facts likely filtered out


Evaluation

Evaluation

  • 26 ontologies on university student domain

  • Measure resultant fact KB vs Reference KB


Related work

Related Work:

  • schema mediation, schema reconciliation, schema matching, semantic coordination, semantic mapping, and ontology mapping

  • ONION, PROMPT, LSD, GLUE, Automatch, SemInt, CUPID, COMA, MGS-DCM, HSDM Mediator, MOBS…

  • Name similarity, Structure similarity, Domain Constraints, Instance Features, Instance similarity, Multi-strategy learning, Statistical analysis, Alignment reuse.

  • Little work on Peer-to-Peer Alignment


Summary

Summary

  • An Alignment Approach:

    • Ontology Alignment carried out by end users in a Peer to Peer fashion

    • Peers are both alignment consumer and producer

  • Future work:

    • Detailed experiments, theoretical analysis

    • Property alignment with class as context

Thank You!


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