Query Translation for Ontology-extended Data Sources

July 23,2007, Semantic e-Science Workshop @AAAI 2007, Vancouver, Canada 1

Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory

Query Translation for Ontology-extended Data Sources

Jie Bao1, Doina Caragea2, Vasant Honavar1

1Artificial Intelligence Research Laboratory,Department of Computer Science,

Iowa State University, Ames, IA 50011-1040, USA{baojie, honavar}@cs.iastate.edu

2Department of Computing and Information SciencesKansas State University, Manhattan, KS 66506, USA

{dcaragea}@ksu.edu



INDUS Group

Vasant Honavar Jie BaoDoina Caragea

Jyotishman Pathak Neeraj Koul



Outline

• Ontology-Extended Data Source– Schema, Data, and Ontology

• Query Translation for OEDS– Ontology mapping, query translation / soundness / completeness

• Implementation and Optimization– The INDUS system

• Conclusion



BackgroundData revolution• Bioinformatics

– Over 200 data repositories of interest to molecular biologists alone• Environmental Informatics• Enterprise Informatics • Medical Informatics• Social Informatics ...

Connectivity revolution (Internet and the web)

Integration revolution • Need to understand the elephant as opposed to examining

the trunk, the tail, etc.

Needed – infrastructure to support collaborative, integrative analysis of data



Solution: INDUS for Learning from Semantically Heterogeneous Distributed Autonomous Data Sources



(Relational) Data Source

DData Set

Extensional Definition(Facts)

MScBob

First-yearAlice

statusname

Student

algorithmCS511

data structureCS103

namecode

Classes

CS511Bob

CS103Alice

classinstructor

Registers

SSchemaIntensional Definition

Classes

Faculty Teaches

name:String

code:String

rank:String

name:StringStudent Registers

name:String status:String



Semantic Extensions of Data Sources

Return classes that graduate students are registered in

Return all people in the database

?

?

DS

MScBob

First-yearAlice

statusname

Student

algorithmCS511

data structureCS103

namecode

Classes

CS511Bob

CS103Alice

classinstructor

Registers



Ontology-Extended Data Source

Classes

Instructor Teaches

name:String

code:String

rank:String

name:StringStudent registers


People

Student Instructor

MScBob

First-yearAlice

statusname

Student

student

Undergrad Graduate

First-year

MSc

Fourth-year

…PhD

MA




DData Set

SSchemaOS Schema Ontology

OD

Data Content Ontology

O’S

O’D




• Relational Model (Reiter, 1982)

– Schema S: a first order language with predicate symbols RS, each for a relational table (e.g. Classes, Faculty)

– Data Set D: a first order interpretation of S with domain

• Ontology-Extended (Relational) Data Source (Caragea et al. 2004)– Extending relational model with

– Schema Ontology: a first order language LOS with predicate symbols ROS, and RS ROS

– Data Content ontology: OOD=(LOD,DOD)

• LOD: a first order language with predicate symbols ROD, ROD RS=

• DOD: a first order interpretation of LOD with domain ’, ’



OEDS: Example

S: Instructor(x,y); Classes(x,y), Student(x,y)…

MScBob

First-yearAlice

statusname

Student

D

Classes

Instructor Teaches

name:String

code:String

rank:String

name:StringStudent registers


LOS

x,y, Student(x,y) Instructor(x,y) People(x)

isa(x,y) isa(y,z) isa(x,z)

LODDOD

isa(First-year,Undergraduate)isa(Undergraduate,Student)isa(MSc,Graduate)…

see survey [Shvaiko & Euzenat 2005]

OD



Outline




• Conclusion



Query

• Tuple Relational Calculus (TRC)– Tuple: a multiset of attributes– TRC Relational Algebra – q(t) := Student(t) (t.status=”Graduate”)

• Ontology-Extended Tuple Relational Calculus– q(t) := Student(t) isa(t.status, Graduate)

We focus on data content ontologies in this talk



Query Translation

DS

O2

q’

DS

q

O1

User Ontology Data Source Ontology

M

Ontology Mapping



Ontology Mapping

isa1(x, c1) ^ into(c1, c2) isa2(x, c2)

isa1(c1, x) ^ onto(c1, c2) isa2(c2, x)

……

Student

Undergrad Graduate

First-year

MSc

Fourth-year

…PhD

MA

Student

Undergrad Postgraduate

Freshman…

DoctoralMaster

into

onto

equ

isa1 isa2



Query Translation

DS

O2

q’q

DS

O1

M

Student(t) ^ isa1(t:status,Master) Student(t) ^ isa2(t.status, Graduate)

Student(t) ^ isa2(t.status, MSc)



Soundness, Completeness and Exactness

{q}

{q’}

{q’}

{q} {q}={q’}

SoundTranslation

CompleteTranslation

ExactTranslation

q := Student(t) ^ isa1(t:status,Master)

q’ := Student(t) ^ isa2(t.status, MSc)

q’ := Student(t) ^ isa2(t.status, Graduate) Non-existent



Most Informative Translation

c1d1

d2

O1O2

q := isa1(x,c1)

isa2(x,d1) isa2(x,d2)Most informative sound translation!

onto

onto

LUB (least upper bound)

isa2(x,d1)

isa2(x,d2)find its sound translation(s)



Query Translation Rules

For hierarchical ontologies

(similarly for complete translation of complex queries)

Atomic conditions

Complex conditions

GLB=greatest lower bound, LUB=least upper bound



Outline




• Conclusion



INDUS Tools

Ontology Editor

Schema Editor

Mapping Editor

Data Editor

Query Engine and Interface

…



INDUS – Mapping Editor

http://sourceforge.net/projects/indus-project/



INDUS – Data Editor




INDUS – Query Editor




Optimization for Scalability

• Database storage for ontologies• Using transitive closure for fast inference with

hierarchies• Server-side caching

– Using temporary tables on the data source server

• Client-side caching – Of remote ontologies and ontology mappings



Performance

• O1: Enzyme Classification (EC) hierarchy (4,564 terms)• M: SCOP to EC mapping [Richard George et. al.] with 15,765 rules• O2: SCOP (Structural Classification of Proteins) hierarchy (86,766 terms).

ServerClient

D

Internet



Performance



Outline




• Conclusion



Conclusion

• We have studied the query translation process for relational data sources extended with context-specific data content ontologies.– how to exploit ontologies and mappings for flexibly querying

semantic-rich data sources.– query translation strategy that works for hierarchical ontologies. – the conditions under which the soundness and completeness of

such a procedure can be guaranteed.

• Ongoing Work– More expressive ontologies, e.g., Description Logics– Schema ontology + data content ontology– Statistical learning from OEDS



Thank You!



Semantics Preserving Translation

• Conservative Extension

Education

Query Translation for Ontology-extended Data Sources