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Baoshi Yan, P2PKM 20057/17/2005 1
Grass-Roots Class Alignment
Baoshi Yan
Information Sciences Institute, University of Southern California
7/17/2005 2Baoshi Yan, P2PKM 2005
Motivation
Sharing Structured Data among peers However, peers might use different
terminology (Ontology)
Need Ontology Alignment
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What is Alignment
Correspondence between concepts
PhDStudent Firstname
Lastname
major
DoctoralStudent Givenname
Familyname
specialization
…
…
…
…
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Alignment: State of the Art
Heuristics-based: Name similarity Structure similarity Instance Constraints Co-occurrence
Domain Expert Centralized Precise Alignment
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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
Peer-to-Peer Alignment
Alignment Corpus
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Grass-roots Alignment Example: WebScripter tool
Inferred Alignment:iswc:phone = isi: phonenumber
Inferred Alignment:iswc:phone = isi: phonenumber
when a user puts different stuffs when a user puts different stuffs into the same column, they mean same thinginto the same column, they mean same thingInferred Alignment:iswc:Person = isi: Div2Member
Inferred Alignment:iswc:Person = isi: Div2Member
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Properties of Grass-Roots Alignment
Might be Approximate
inconsistent
Intransitive
GraduateO1
DoctoralStudent
PhDStudent
GraduateStudent
MSStudent
O2
MasterStudent
O3 O4
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Challenge
How to reuse approximate or inconsistent grass-roots alignments for alignment purposes
Approximation conservative semantics of alignment
Inconsistency evidences
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Observations & Assumptions
Users tend to pick closest alignment candidate
O2 O2
A
B
CA
CB
O1A
B C
A C
B
O1
A
B C
O1A
B
C
B
C
AO1A
B
C(a) (b)
(c) (d)
O2O2
O2 O2
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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
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Class Alignment Algorithm:Step 1
Subclass Relationships Specified in the Ontology
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Class Alignment Algorithm:Step 2
Class Relationships Implied by Grass-roots Alignments: the Semantics of Grass-roots Alignments:
A
B CA
B
C
A
C
BOR
C
B
AA
B C
A
B C
NOT NOT, ,O1 O2
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the Semantics of Grass-roots Alignments (Cont)
A
B
C A
C
BNOT
O1 O2
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the Semantics of Grass-roots Alignments (Cont)
A
B C
D A · D
B · C
O1 O2
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Class Alignment Algorithm:Step 2
Class Relationships Implied by Alignments
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Class Alignment Algorithm:Step 3: Forward-chaining Inference
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(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)
Dealing with Evidences
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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 BA, remove B from Sup(A). Do the same to Sub(A).
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Examples: Ind(MasterStudent)={
MSStudent}
Sup(MasterStudent)={Graduate,Student,UnivStudent}
Sub(Graduate)={MasterStudent,MSStudent,DoctoralStudent}
Class Alignment Using Facts KB (cont)
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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.
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Class Alignment Using Facts KB (cont)
Example: Ind(MasterStudent)={MSStudent} Sup(DoctoralStudent)={Graduate,Student,UnivStudent} Ind(Student)={UnivStudent}
Student
O1 O2
DoctoralStudentMaster
Student
UnivStudent
Graduate
MSStudent
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Evaluation (qualitative analysis)
In the ideal case: Each previous alignment is best possible Then: Guaranteed Correctness in some cases
In the not-so-ideal case: Bad facts likely filtered out
Student
O1
DoctoralStudent
UnivStudent
Graduate
O2
Sup(DoctoralStudent)= {UnivStudent,Graduate}
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Evaluation
Performance on University Student Ontology Set
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40
Number of Alignments
Recall-Single-Inheritance
Precision-Single-Inheritance
Recall-Multi-Inheritance
Precision-Multi-Inheritance
26 ontologies on university student domain Measure resultant fact KB vs Reference KB
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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
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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!
