1. Baoshi Yan, P2PKM 2005 7/17/2005 1 Grass-Roots Class Alignment Baoshi Yan Information Sciences Institute, University of Southern California

2. Baoshi Yan, P2PKM 2005 7/17/2005 2 Motivation  Sharing Structured Data among peers  However, peers might use different terminology (Ontology) Need Ontology Alignment

3. Baoshi Yan, P2PKM 2005 7/17/2005 3 What is Alignment  Correspondence between concepts PhDStudent Firstname Lastname major DoctoralStudent Givenname Familyname specialization … … … …

4. Baoshi Yan, P2PKM 2005 7/17/2005 4 Alignment: State of the Art  Heuristics-based:  Name similarity  Structure similarity  Instance  Constraints  Co-occurrence  Domain Expert  Centralized  Precise Alignment

5. Baoshi Yan, P2PKM 2005 7/17/2005 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 Peer-to-Peer Alignment Alignment Corpus

6. Baoshi Yan, P2PKM 2005 7/17/2005 6 Grass-roots Alignment Example: WebScripter tool Inferred Alignment: iswc:phone = isi: phonenumber Inferred Alignment: iswc:phone = isi: phonenumber when a user puts different stuffs into the same column, they mean same thing Inferred Alignment: iswc:Person = isi: Div2Member Inferred Alignment: iswc:Person = isi: Div2Member

7. Baoshi Yan, P2PKM 2005 7/17/2005 7 Properties of Grass-Roots Alignment  Might be  Approximate  inconsistent  Intransitive Graduate O1 Doctoral Student PhDStudent Graduate Student MSStudent O2 Master Student O3O4

8. Baoshi Yan, P2PKM 2005 7/17/2005 8 Challenge  How to reuse approximate or inconsistent grass-roots alignments for alignment purposes  Approximation  conservative semantics of alignment  Inconsistency  evidences

9. Baoshi Yan, P2PKM 2005 7/17/2005 9 Observations & Assumptions  Users tend to pick closest alignment candidate O2 A B CA CB O1 A BC AC B A BC A B C B C A A B C (a)(b) (c) (d) O2

10. Baoshi Yan, P2PKM 2005 7/17/2005 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. Baoshi Yan, P2PKM 2005 7/17/2005 11 Class Alignment Algorithm: Step 1  Subclass Relationships Specified in the Ontology

12. Baoshi Yan, P2PKM 2005 7/17/2005 12 Class Alignment Algorithm: Step 2  Class Relationships Implied by Grass- roots Alignments: the Semantics of Grass-roots Alignments: A BC A B C A C B OR C B A A BC A BC NOT,, O1O2

13. Baoshi Yan, P2PKM 2005 7/17/2005 13 the Semantics of Grass-roots Alignments (Cont) A B C A C B NOT O1O2

14. Baoshi Yan, P2PKM 2005 7/17/2005 14 the Semantics of Grass-roots Alignments (Cont) A BC DA · D B · C O1O2

15. Baoshi Yan, P2PKM 2005 7/17/2005 15 Class Alignment Algorithm: Step 2  Class Relationships Implied by Alignments

16. Baoshi Yan, P2PKM 2005 7/17/2005 16 Class Alignment Algorithm: Step 3: Forward-chaining Inference

17. Baoshi Yan, P2PKM 2005 7/17/2005 17  (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

18. Baoshi Yan, P2PKM 2005 7/17/2005 18 Class Alignment Algorithm Step 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. Baoshi Yan, P2PKM 2005 7/17/2005 19  Examples:  Ind(MasterStudent)= {MSStudent}  Sup(MasterStudent) ={Graduate,Student, UnivStudent}  Sub(Graduate)={Mas terStudent,MSStude nt,DoctoralStudent} Class Alignment Using Facts KB (cont)

20. Baoshi Yan, P2PKM 2005 7/17/2005 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. Baoshi Yan, P2PKM 2005 7/17/2005 21 Class Alignment Using Facts KB (cont)  Example:  Ind(MasterStudent)={MSStudent}  Sup(DoctoralStudent)={Graduate,Student,UnivStudent}  Ind(Student)={UnivStudent} Student O1O2 Doctoral Student Master Student UnivStudent Graduate MSStudent

22. Baoshi Yan, P2PKM 2005 7/17/2005 22 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 Doctoral Student UnivStudent Graduate O2  Sup(DoctoralStudent)=  {UnivStudent,Graduate}

23. Baoshi Yan, P2PKM 2005 7/17/2005 23 Evaluation  26 ontologies on university student domain  Measure resultant fact KB vs Reference KB

24. Baoshi Yan, P2PKM 2005 7/17/2005 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. Baoshi Yan, P2PKM 2005 7/17/2005 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!