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Grass-Roots Class Alignment

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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  1. Grass-Roots Class Alignment Baoshi Yan Information Sciences Institute, University of Southern California

  2. Motivation • Sharing Structured Data among peers • However, peers might use different terminology (Ontology) Need Ontology Alignment

  3. What is Alignment • Correspondence between concepts

  4. Alignment: State of the Art • Heuristics-based: • Name similarity • Structure similarity • Instance • Constraints • Co-occurrence • Domain Expert • Centralized • Precise Alignment

  5. 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

  6. 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

  7. O2 O1 Graduate GraduateStudent PhDStudent MSStudent O3 O4 Doctoral Student Master Student Properties of Grass-Roots Alignment • Might be • Approximate • inconsistent • Intransitive

  8. Challenge • How to reuse approximate or inconsistent grass-roots alignments for alignment purposes • Approximation • conservative semantics of alignment • Inconsistency • evidences

  9. 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

  10. 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

  11. Class Alignment Algorithm:Step 1 • Subclass Relationships Specified in the Ontology

  12. 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:

  13. the Semantics of Grass-roots Alignments (Cont) O1 O2 A A C B NOT C B

  14. O1 O2 A D A · D B C B · C the Semantics of Grass-roots Alignments (Cont)

  15. Class Alignment Algorithm:Step 2 • Class Relationships Implied by Alignments

  16. Class Alignment Algorithm:Step 3: Forward-chaining Inference

  17. 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)

  18. 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).

  19. Class Alignment Using Facts KB (cont) • Examples: • Ind(MasterStudent)={MSStudent} • Sup(MasterStudent)={Graduate,Student,UnivStudent} • Sub(Graduate)={MasterStudent,MSStudent,DoctoralStudent}

  20. 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.

  21. 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

  22. 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

  23. Evaluation • 26 ontologies on university student domain • Measure resultant fact KB vs Reference KB

  24. 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

  25. 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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