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CIS607, Fall 2005CIS607, Fall 2005

Semantic Information Semantic Information IntegrationIntegration

Instructor: Dejing Dou

Week 2 (Oct. 5)

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OutlineOutline

The Differences and Correspondences between Ontologies/Schemas

Ontology and Schema Mapping/MatchingOntology and Schema Integration/MergingData Translation and Data IntegrationSemantic Query Processing and Semantic

Search

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The Differences and Correspondences The Differences and Correspondences between Schemas between Schemas

Schema 1 Schema 2

OfficeAddress BusinessAddress

Street

CityState

ZIP

Street

CityUSState

PostalCode

Customer AccountOwner

FirstName

LastName

FullName

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The Differences and Correspondences The Differences and Correspondences between Ontologies from two DBsbetween Ontologies from two DBs

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The Differences and Correspondences The Differences and Correspondences between Semantic Web Ontologies 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

MaleFemale

husband

wife

Gendersex

Individual

Family

MaleFemale

Gendersex

spouseIn

DRC_ged BBN_ged

Better ?

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The Differences and Correspondences between The Differences and Correspondences between Semantic Web Ontologies(cont’d)Semantic Web Ontologies(cont’d)

Simple semantic differences because of different class hierarchy.

Publication

Book Thesis

TechReportArticle

Publication

Proceedings

Collection

Book

Thesis

Techreport

Incollection

Inproceedings

Article (in Journal)

The class hierarchies of two bibliography ontologies

CMU_bib Yale_bib

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The Differences between Ontologies on The Differences between Ontologies on Similar Domain (cont’d)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

Book Publication String String

booktitle booktitle

RussellNorvig95 “Artificial Intelligence: A Modern Approach”

Minsky77 “Proceedings of IJCAI 77”

CMU_bib Yale_bib

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Ontology and Schema Mapping/MatchingOntology 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).

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Approaches for Finding Matchings 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.

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

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

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

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Approaches for Representing Approaches for Representing Matching/Mappings 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))

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Approaches for Representing Mappings Approaches for Representing Mappings (cont’d) (cont’d)

Instance of Ontology [Maedche etal02].– E.g. <AttributeBridge rdf:ID = “zip-postalcode”>

<relatesSourceEntity rdf:resource = “#zip”/>

<relatesTargetEntity rdf:resource = “#postalcode”/>

<accordingToTransformation rdf:resouce=“#copyName”/>

</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)))))

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Ontology and Schema Integration/MergingOntology 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

Source Target

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Approaches for Merging Ontologies Approaches for Merging Ontologies (Schemas) (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.

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Approaches for Merging Ontologies Approaches for Merging Ontologies (Schemas) (cont’d) (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]

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Data Integration and TranslationData 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 OBData in OA

Data in M_A_B

Data in OBData in OA

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Approaches for Data Integration/TranslationApproaches 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. <Firstname(John) LastName(Smith)>

=> 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.

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Approaches for Data Integration/Translation Approaches for Data Integration/Translation (cont’d)(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)

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Semantic Query Processing and Search 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 (<Yo-Yo Ma>, birthplace) => <Paris, France>

Queries expressed in OA

KB in OB

TranslatorQuery in OB Bindings

KB in OC…… ……

Query in OC Bindings

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Approaches for Semantic Query Processing 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]

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Approaches for Semantic Search 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

10/07/55 Paris, France

birthdate

birthplace

type

http://tap.stanford.edu/data/MusicianMa,_Yo_Yohttp://tap.stanford.edu/data/CityParis,_France

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Presentation ArrangementPresentation 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)