1. 1 CIS607, Fall 2005 Semantic Information Integration Instructor: Dejing Dou Week 2 (Oct. 5)

2. 2 Outline The Differences and Correspondences between Ontologies/Schemas Ontology and Schema Mapping/Matching Ontology and Schema Integration/Merging Data Translation and Data Integration Semantic Query Processing and Semantic Search

3. 3 The Differences and Correspondences between Schemas Schema 1Schema 2 OfficeAddressBusinessAddress Street City State ZIP Street City USState PostalCode CustomerAccountOwner FirstName LastName FullName

4. 4 The Differences and Correspondences between Ontologies from two DBs

5. 5 The Differences and Correspondences between Semantic Web Ontologies Syntactic differences because of different languages. Simple semantic differences because of different taxonomic structure for properties. husband and wife vs. spouseIn Individual Family Male Female husband wife Gender sex Individual Family Male Female Gender sex spouseIn DRC_ged BBN_ged Better ?

6. 6 The Differences and Correspondences between Semantic Web Ontologies(cont ’ d) Simple semantic differences because of different class hierarchy. Publication Book Thesis TechReport Article Publication Proceedings Collection Book Thesis Techreport Incollection Inproceedings Article ( in Journal ) The class hierarchies of two bibliography ontologies CMU_bib Yale_bib

7. 7 The Differences between Ontologies on Similar Domain (cont ’ d) Complicated semantic differences: – Different meanings for the concepts even using same name (Homonyms). – Differences inherited from those between basic concepts in some super ontologies, such as time, space etc. e.g. MarriageEvent <- Event <- Date <- Time BookPublicationString booktitle RussellNorvig95 “Artificial Intelligence: A Modern Approach” Minsky77 “Proceedings of IJCAI 77” CMU_bib Yale_bib

8. 8 Ontology and Schema Mapping/Matching How to find the correspondences (matchings) between the concepts of different ontologies or schemas. How to represent the founded correspondences (matchings) as mappings (relationships in formal form, e.g., mapping rules).

9. 9 Approaches for Finding Matchings Similarity matching for the same or similar names for concepts. – E.g. City  City, FirstName  FullName Exploiting synonyms and is-a (part of) relationships using thesauri and dictionary[Serafini etal03], such as Wordnet. – E.g. ZIP  PostalCode, husband  spouseIn Machine learning from data instances[Doan etal02]. – E.g. Phone (541-346-4572)  Tel (541-346-4572) Calculate P(Phone, Tel), the joint probability as the faction of the instance universe belongs to both A (Phone) and B (Tel) by machine learning.

10. 10 Machine learning from data instances[Doan etal 02]

11. 11 Discover Complex Matchings by Search [Dhamankar etal 04]

12. 12 Discover Complex Matchings by Correlation Data Mining [He etal 04]

13. 13 Approaches for Representing Matching/Mappings Probability or Similarity. – E.g. P(city, lastname) = 10% P(phone, tel) = 99% Query languages based on views[Madhavan etal02]. – E.g. create view StoN.Customer (fullname, accountnumber) select concat(firstname, lastname) as fullname, accountnumber from Stores7.Customer Rewriting rules [Chalupsky00]. – E.g. (defruleset get_fullname (AND (FirstName ?x) (LastName ?y)) = => (FullName ?x ?y))

14. 14 Approaches for Representing Mappings (cont ’ d) Instance of Ontology [Maedche etal02]. – E.g. </AttributeBridge Expressive Logic Rules [Dou etal 03] – E.g. (forall (a - @yale_bib:Inproceedings tl - String) (if (@yale_bib:booktitle a tl) (exists (p - Proceedings) (and (@cmu_bib:inProceedings a p) (@cmu_bib:booktitle p tl)))))

15. 15 Ontology and Schema Integration/Merging The process of combining two ontologies or schemas to a bigger one to cover the concepts from original ontologies or schemas. – Find the mappings or use the founded mappings – Combine the concepts based on mappings – Check Consistency SourceTarget

16. 16 Approaches for Merging Ontologies (Schemas) Chimæra [McGuinness etal00]. – Create class taxonomies from web ontologies. – Find matchings from name similarity and taxonomies. – Merging suggestions and editing operations (adding, deleting, renaming) by GUI. – Diagnostics tests (completeness, syntactic and taxonomic analysis, semantic evaluation). PROMPT [NoyMusen00]. – Find matchings by linguistic similarity or user plug in. – Make Initial Merging Suggestions. – Perform automatic updates – Make suggestions again after consistency checking.

17. 17 Approaches for Merging Ontologies (Schemas) (cont ’ d) Merge Models Based on given Correspondences [PB03]. – Merge A and B based on Map_AB as a function: Merge(A, Map_AB, B) => G. – Mappings of A and B are more expressive than correspondences. – Resolve conflicts by automatic algorithms. Merge ontologies by FOL Bridging Axioms [Dou etal03]. – Just combine the concepts of source and target ontologies together but use namespaces to distinguish them. – Use bridging axioms to express the relationship (mappings) of the concepts in one ontology to the concepts in the other. Combine ontologies by Distributed DL, ε-Connections and OWL reasoners [Grau etal 04]

18. 18 Data Integration and Translation Integrate data from distributed resources to a merged (mediated) ontologies or schemas. Translate/Exchange data from one ontology (schema) to another one. There are some commercial Enterprise Information Integration systems but not good at semantic heterogeneity [Halevy etal 05] Data in O B Data in O A Data in M_A_B Data in O B Data in O A

19. 19 Approaches for Data Integration/Translation OntoMorph [Chalupsky00]. – Translating data from ontology to another by applying rewriting rules (Pattern => Result) to input data until no more rules need to be applied. e.g. => Fullname (John Smith) Construct special translators by using self-defined translation rules[Abiteboul etal02]. – The rules can be written in rule-based languages for objects, e.g. IQL, LDL, F-logic. – Translation rules have some restrictions to guarantee decidability.

20. 20 Approaches for Data Integration/Translation (cont ’ d) OntoEngine [Dou etal03]. – Use a first order inference engine to implement data translation by forward chaining. The translation rules (bridging axioms) are in formal first order logic. e.g. (forall (f - Family h - Individual m - Marriage) (if (and (@bbn_ged:sex h "M") (@bbn_ged:spouseIn h f) (@bbn_ged:marriage f m)) (and (@drc_ged:husband f h) (@drc_ged:marriage f m)) 21164 facts in bbn_ged OntoEngine 26956 facts in drc_ged (@bbn_ged:sex Henry_VI "M") (@bbn_ged:spouseIn Henry_VI @royal92:F456) (@bbn_ged:marriage @royal92:F456 @royal92:event3138) (@drc_ged:husband @royal92:F456 Henry_VI) (@drc_ged:marriage @royal92:F456 @royal92:event3138)

21. 21 Semantic Query Processing and Search Translating (Rewriting) query from one schema (ontology) to another. Doing search on the Semantic Web. – Traditional search just based on text matching, many redundant or even wrong results. – Get Data based on the query with formally defined semantics. e.g. GetData (, birthplace) => Queries expressed in O A KB in O B Translator Query in O B Bindings KB in O C …… Query in O C Bindings

22. 22 Approaches for Semantic Query Processing Information Integration using logic views [Ullman00]. – Find answers for a global query in mediated concepts. – Express the query in views from local data resources and get answers. e.g. Q(P,O) <= phone(John, P) & office (John, O) answer(P,O) <= v1(John, P, M) & v2 (John, O, D) answer(P, O) <= v3(John, P) & v2 (John, O, D) Answering Query using views [PottingerLevy00]. – Express the original query in logic views. – Find (translate to) an equivalent query based on the relationships of views from different databases. Query Processing in LAV and GAV [Lenzerini 02]

23. 23 Approaches for Semantic Search TAP, an application framework on Semantic Search [Guha etal]. – All web documents have been marked up by RDF based languages. Therefore, each web data resource has a URI or value and whole web document can be represents as a graph with nodes (classes, values) and arcs (property relation). author Tavener Yo-Yo Ma Musician 10/07/55Paris, France birthdate birthplace type http://tap.stanford.edu/data/MusicianMa,_Yo_Yo http://tap.stanford.edu/data/CityParis,_France

24. 24 Presentation Arrangement Ontology and Schema Mapping/Matching – Enrico (Glue, iMAP), Dayi (KDD matchings) Ontology and Schema Integration/Merging – Amanda (Prompt) Data Translation and Data Integration – Paea (Theoretical), Zebin (OntoMerge), Shiwoong (EII) Semantic Query Processing and Semantic Search - Jiawei( Semantic Search)