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Integrating Databases into the Semantic Web Integrating Databases into the Semantic Web through an Ontology-based Frameworkthrough an Ontology-based Framework

Dejing Dou, Paea LePendu, Shiwoong Kim

Computer and Information Science, University of Oregon, USA

Peishen Qi

Computer Science Department, Yale University, USA

April, 2006 @ SWDB’06

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OutlineOutline Introduction

– The status of the Semantic Web– Realizing SW needs existing databases

OntoGrate: An Ontology-based Information Integration Framework– Some previous work– Modules in OntoGrate Architecture

Case Study for integrating Databases into SW– Without an existing domain ontology– With an existing domain ontology

Conclusion and Future Work

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The Semantic Web The Semantic Web One major goal of the Semantic Web is that web-based agents can

process and “understand” data [Berners-Lee etal01]. Ontologies formally describe the semantics of data and web-based

agents can take SW documents (e.g. in RDF/OWL) as a set of assertions (true statements) and draw inferences from them.

human

SW

Web-based agents

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What we have now?What we have now? DAML+OIL OWL (Web ontology language)

More and more domain ontologies are defined in DAML+OIL/OWL, even for some specific domains (e.g., GO)

We are developing some tools, agents, services

See http://www.semwebcentral.org, http://knowledgeweb.semanticweb.org/

http://www.daml.org/

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Two things are importantTwo things are important

Real Data for sharing– relational databases (may be the biggest resource)– Other kinds of databases– WWW/XML data– Some knowledge bases

Better Semantic Web Services/Agents

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Semantic Annotation for Data?Semantic Annotation for Data? It is good for small size data resources

It is not that good for large size data resources (relational databases) – “Redundant” copies – Time consuming for query answering.

E.g. it currently works as loading OWL data into a knowledge base then answering queries with DL ABox reasoning. (Can it compete with existing DBMS which has well developed indexing and query optimization techniques?)

It is better that relational databases can be accessed/queried directly by SW agents/services

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The difficultiesThe difficulties

The Semantic Web The Relational DBs

Ontologies define the semantics of data

Schemas define the structure and integrity constraints

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A more general questionA more general question

How can we make databases, SW resources, WWW/XML data, KBs work together?

The problem is similar– SW resources and KBs are defined by ontologies, which

are more expressive and focus on semantics– Databases and XML documents are defined by schemas,

which focus on structure– Syntax difference (e.g., OWL vs. SQL)

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OntoGrate: An Ontology-based Information OntoGrate: An Ontology-based Information Integration SystemIntegration System

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Some Previous WorkSome Previous Work Schemas (e.g., stores7 DB in IBM informix),

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Some Previous WorkSome Previous WorkSchemas, Ontologies and Web-PDDL

Relation Type/Class

Attribute Predicate/Property

Integrity Constrain Axiom/Rule

Primary Key Fact/Instance

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Some Previous WorkSome Previous WorkMerging Ontologies with Bridging Axioms

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Some Previous WorkSome Previous WorkThe Bridge Axiom/mapping on customerfname/customerlname vs. customercontactname :

(forall (c - @stores7:Customer f l - @sql:varchar) (if (and (@stores7:customerfname c f)

(@stores7:customerlname c l))

(@nwind:customercontactname c (@sql:concat f l))))

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Some Previous WorkSome Previous WorkThe Bridge Axiom/mapping on customerregion vs. customerstatecode/statename/statecode :

(forall (x - @nwind:Customer y - @sql:varchar) (if (@nwind:customerregion x y) (exists (z - @stores7:State t - @sql:varchar) (and (@stores7:customerstatecode x t) (@stores7:statename z y) (@stores7:statecode z t)))))

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Some Previous WorkSome Previous WorkInferential Data Integration with OntoEngine

– Data Translation:

View data as true statements, e.g., (statecode S#28 “OR”)

(Ms_t; s) D t only if (Ms_t; s) ╞ t

(Ms_t; s) D t (Ms_t; s) ├ t (Ms_t; s) ╞ t

– Query Translation:

(Ms_t; s) Q t only if (Ms_t; (t)) ╞ (s)

(Ms_t; s) Q t (Ms_t; (t)) ├ (s)

(Ms_t; (t)) ╞ (s)

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OntoGrate Architecture RevisitedOntoGrate Architecture Revisited

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Modules in OntoGrate ArchitectureModules in OntoGrate ArchitectureThe Syntax Translators (Wrappers)

– e.g., PDDSQL (SQLWeb-PDDL), PDDOWL(OWL Web-PDDL)

The Matching (correspondence) Generation– e.g., name, structure (tree, graph) similarity,synonyms and

is-a (part of) relationships using thesauri and dictionary, such as Wordnet

The Data Mining ModuleThe Machine Learning ModuleThe Inference Engine (OntoEngine)The User Interface

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Learning the mappings from Learning the mappings from domain expertsdomain experts

(forall (x - @A1:Invertebrate)

(if (is @A1:Insect x)

(and (@A2:legs x 6)

(@A2:bodySegments x 3))))

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Mining the mappings from large Mining the mappings from large datasetsdatasets

For example, two Medical databases in the same hospital: DB1 list blood pressure of patients with nominal values, such as low, normal, at risk, and high, while the other DB2 may record the exact numerical values for systolic and diastolic pressure.

By association rule mining, we may get the rule/mapping like:

@DB2:SystolicPressure 140 @DB2:DiastolicPressure 90

@DB2:BloodPressure = `High‘

(support = 40%, confidence = 90%)

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Case Study in Two ScenariosCase Study in Two ScenariosIntegrating DBs into SW without an

existing domain ontology

Integrating DBs into SW with an existing domain ontology

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Without an existing domain ontologyWithout an existing domain ontology

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Generating OWL ontologies from DB SchemasGenerating OWL ontologies from DB Schemas

SQL schema Web-PDDL (by using PDDSQL) Web-PDDL OWL (by using PDDOWL)

– E.g., Stores7.sql Stores7.pddl Stores7.owl ... <owl:Class rdf:ID="Customer"> <rdfs:subClassOf rdf:resource=“http://www.cs.uoregon.edu/~paea/sql#Relation"/> </owl:Class> <owl:DatatypeProperty rdf:ID="customercity"> <rdfs:domain rdf:resource="#Customer"/> <rdfs:range rdf:resource="#String"/> </owl:DatatypeProperty> ...

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An OWL-QL query based on Stores7.owl An OWL-QL query based on Stores7.owl <owl-ql:query xmlns:owl-ql=“http://www.w3.org/2003/10/owl-ql-syntax#"...> <owl-ql:premise> <rdf:RDF> <rdf:Description rdf:about="#C"> <rdf:type rdf:resource="#Customer"/> <customercity rdf:resource="#Eugene"/> </rdf:Description> </rdf:RDF> </owl-ql:premise> <owl-ql:queryPattern> <rdf:RDF> <rdf:Description rdf:about="#C"> <customerfname rdf:resource="http://www.w3.org/2003/10/ owl-ql-variables #x"/> <customerlname rdf:resource=" http://www.w3.org/2003/10/ owl-ql-variables##y"/> </rdf:Description> </rdf:RDF> </owl-ql:queryPattern> <owl-ql:answerKBPattern> <owl-ql:kbRef rdf:resource="...stores7.owl"/>…

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The corresponding Web-PDDL and SQL queriesThe corresponding Web-PDDL and SQL queries

(and (customercity ?C - Customer "Eugene") (customerfname ?C - Customer ?x - String) (customerlname ?C - Customer ?y - String))

PDDSQL

SELECT C.customerfname, C.customerlname

FROM Customer C

WHERE C.customercity = "Eugene"

PDDOWL

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Getting Answers from Stores7 DBGetting Answers from Stores7 DB

{?x/Paea, ?y/LePendu}{?x/Dejing, ?y/Dou}{?x/Shiwoong, ?y/Kim}

PDDOWL

PDDSQL

customerfname customerlname

Paea LePendu

Dejing Dou

Shiwoong Kim

<owl-ql:answerBundle

xmlns:owl-ql=" http://www.w3.org/2003/10/ owl-ql-syntax#" ...>

<owl-ql:answer>

<owl-ql:binding-set>

<var:x rdf:resource="#Paea"/>

<var:y rdf:resource="#LePendu"/>

</owl-ql:binding-set>

<owl-ql:answerPatternInstance>

<rdf:RDF>

<rdf:Description rdf:about="#C">

<customerfname rdf:resource="#Paea"/>

(1000 bindings/3 secs)

(1000/100,000/3secs)

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With an existing domain ontologyWith an existing domain ontology

Order ontology: http://www.dayf.de/2004/owl/order.owl

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An OWL-QL query based on order.owl An OWL-QL query based on order.owl <owl-ql:query xmlns:owl-ql=“http://www.w3.org/2003/10/owl-ql-syntax#"...> <owl-ql:premise> <rdf:RDF> <<rdf:type rdf:resource="#Person"/> <hasAddress rdf:resource="#A"/> </rdf:Description> <rdf:Description rdf:about="#A"> <rdf:type rdf:resource="#Address"/> <City rdf:resource="#Eugene"/> </rdf:Description> </rdf:Description> </rdf:RDF> <owl-ql:queryPattern> <rdf:RDF> <rdf:Description rdf:about="#C"> <FirstName rdf:resource="http://www.w3.org/2003/10/ owl-ql-variables #x"/> <LastNname rdf:resource=" http://www.w3.org/2003/10/ owl-ql-variables##y"/> … <owl-ql:kbRef rdf:resource=" http://www.dayf.de/2004/owl/order.owl"/>…

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The Bridging Axioms/Mappings between The Bridging Axioms/Mappings between Stores7.pddl and Order.pddl Stores7.pddl and Order.pddl

(T-> @stores7:Customer @order:Person)

(forall (P - @order:Person A - @order:Address z - String)

(if (and (@order:hasAddress P A)

(@order:City A z))

(@stores7:customercity P z)))

(forall (C - @stores7:Customer z - String)

(if (@stores7:customercity P z)

(exists (A - @order:Address)

(and (@order:hasAddress P A)

(@order:City A z)))))

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The Bridging Axioms/Mappings between The Bridging Axioms/Mappings between Stores7.pddl and Order.pddl Stores7.pddl and Order.pddl

(T-> @stores7:Customer @order:Person)

(forall (C - @stores7:Customer x - String)

(iff (@stores7:customerfname C x)

(@order:FirstName C x)))

(forall (C - @stores7:Customer y - String)

(iff (@stores7:customerlname C y)

(@order:LastName C y)))

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The Query Translation between Stores7 and OrderThe Query Translation between Stores7 and Order

(and (hasAddress ?C - Person ?A - Address) (City ?A "Eugene") (FirstName ?C - Person ?x - String) (LastName ?C - Person ?y - String))

OntoEngine ( < 1 sec)

(and (customercity ?C - Customer "Eugene") (customerfname ?C - Customer ?x - String) (customerlname ?C - Customer ?y - String))

PDDOWL

Bridging Axioms

OWL-QL query in order.owl

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Final Answers in the order ontologyFinal Answers in the order ontology

(customerfname C1 Paea)(customerlname C2 LePendu)(customerfname C1 Dejing)…

PDDOWL (10,000 facts/11 secs)

PDDSQL

customerfname customerlname

Paea LePendu

Dejing Dou

Shiwoong Kim

<owl-ql:answer>

<owl-ql:binding-set>

<var:x rdf:resource="#Paea"/>

<var:y rdf:resource="#LePendu"/>

</owl-ql:binding-set>

<owl-ql:answerPatternInstance>

<rdf:RDF>

<rdf:Description rdf:about="#C">

<FirstName rdf:resource="#Paea"/>

<LastName rdf:resource="#LePendu"/>

…

OntoEngine (40,000facts/30 secs)

(FirstName C1 Paea)(LastName C2 LePendu)(FirstName C1 Dejing)…

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Some related workSome related work Semantic Annotation

– [Stojanovic etal@SAC02] maps relational model to frame logic/RDF.– DOGMA[Verheyden etal@SWDB04] translates a ontology query to SQL

Schema and Ontology mapping– Similarity matching, machine learning… useful for

generating candidate matchings– Semi-automatic tool (Clio)

Data integration and query answering– Federated databases[Sheth&Larson 90], data warehouse, peer

to peer management [Halevy etal@ICDE03] , MiniCon [PottingerLevy@VLDB00] uses query rewriteing at GLV

Logic and Databases– Reiter’s reconstruction of relational model in FOL.– Carnot, SIMS, Information Manifold by using a global

ontology, DL or Datalog

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Conclusion and Future workConclusion and Future work We applied OntoGrate, an ontology-based information

integration framework, to integrate relational databases with the Semantic Web. The testing result based on two scenarios is promising.

We are developing other modules (e.g., learning/mapping/UI) in OntoGrate.

The scalability and efficiency need to be investigated in larger-size data resources.

Extending the current work to integrate XML (with/without XML schemas or DTD) and the Semantic Web.

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Thank you for your attentionThank you for your attention ! !