DL'12 mastro at work
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Mastro at Work: Experiences onOntology-based Data Access
Domenico Fabio Savo1, Domenico Lembo1,Maurizio Lenzerini1, Antonella Poggi1,
Mariano Rodriguez-Muro2, Vittorio Romagnoli3,Marco Ruzzi1, Gabriele Stella3
1 Sapienza Universitadi Roma
2 Free University ofBozen-Bolzano
[email protected] Banca Monte dei
Paschi di Siena
May, 2010Mastro at Work Savo et. al.
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Motivations
DL-Lite OBDA framework
OBDA
Integrated view, semantically richdescription, mapping for concep-tual level and data sources. Ex-ploiting reasoning to overcome in-completeness
Data SourceData Source
Data SourceData Layer
Ontology Semantic Layer
Queries
Mappings
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Motivations
DL-Lite OBDA framework
DL-Lite framework for OBDA
Components:
• A family of OntologyLanguages: DL-Lite.
• A mapping technique forrelational databases:Virtual ABoxes
• Promising proposal.
• However, never evaluated in‘the field’.
Data SourceData Source
Data SourceData Layer
Ontology Semantic Layer
Queries
Mappings
Mastro at Work Savo et. al.
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Motivations
The domain
• Joint project on OBDA by Banca Monte dei Paschi diSiena (MPS), Free University of Bozen-Bolzano, andSAPIENZA Universita di Roma.
• Clusters of Connected Customers (CCCs)
• Data is used in risk estimation in the process of grantingcredit to bank customers
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Motivations
Problems and Solutions
• management is now completely entrusted to the expertof the applications rather than to the domain experts.
• OBDA has been then used for answering queries posed overthe CCCs ontology, not only aimed at easily extractingrelevant information but also to localize inconsistenciesand incompleteness in the data, as well as to devise newdata governance tasks.
Mastro at Work Savo et. al.
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Motivations
Problems and Solutions
• management is now completely entrusted to the expertof the applications rather than to the domain experts.
• OBDA has been then used for answering queries posed overthe CCCs ontology, not only aimed at easily extractingrelevant information but also to localize inconsistenciesand incompleteness in the data, as well as to devise newdata governance tasks.
Mastro at Work Savo et. al.
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Systems
Mastro at Work Savo et. al.
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Mastro
The Mastro-OBDA plugin
A DL-Lite reasoner for the OBDA context that is able to take anontology with with mappings to a relational database (defining a‘virtual Abox’) in order to provide the following services:
Features
• Conjunctive Query Answering
• Epistemic Query Answering (EQL)
• Identification Constraints
• Epistemic Constraints
Mastro at Work Savo et. al.
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Mastro
The Mastro-OBDA plugin
A DL-Lite reasoner for the OBDA context that is able to take anontology with with mappings to a relational database (defining a‘virtual Abox’) in order to provide the following services:
Features
• Conjunctive Query Answering
• Epistemic Query Answering (EQL)
• Identification Constraints
• Epistemic Constraints
Mastro at Work Savo et. al.
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Protege, OBDA and Mastro plugins
Protege 4 and the OBDA Plugin
Features
• Ontology definition
• Datasource and mappingdefinition
• Interaction withOBDA-reasoner (CQs,Epistemic queries, etc.)
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Case Study
Mastro at Work Savo et. al.
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MPS
Methodology
• Developed the Ontology independently from the source
• Tools used:• interviews• questionnaires• existing documentation
• Developed over a period of 6 months
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Ontology
Excerpt of the Ontology
∃inGrouping v Customer
∃inGrouping− v Grouping∃relativeTo v Grouping
∃relativeTo− v CCC
Grouping v ∃inGrouping−
Grouping v ∃relativeTo(functional relativeTo)
(functional inGrouping−)Grouping v δ(timestamp)
JuridicalCCC v CCCJuridicalCCC v δ(timestamp)
∃inMembership v Customer
∃inMembership− v Membership∃hasMembership v CompanyGroup
∃hasMembership− v Memberhip
∃Membership v ∃inMembership−
Memberhip v ∃hasMembership−
(functional inMembership−)(functional hasMembership)
Holding v MembershipMembership v δ(timestamp)
CompanyGroup v δ(id code)
79 concepts, 33 roles, 37concept attributes, 600DL-LiteA,Id axioms
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Constraints
IDCs to impose complex business constraints
(id JuridicalCCC timestamp, relativeTo−
◦ inGrouping− ◦ inMembership ◦ ?Holding◦ hasMembership−)
• At the same time two juridical CCCs cannot comprisecustomers that are lead members, i.e., are the holdings, of thesame company group.
A total of 30 Identification Constraints
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Constraints
IDCs to impose complex business constraints
(id JuridicalCCC timestamp, relativeTo−
◦ inGrouping− ◦ inMembership ◦ ?Holding◦ hasMembership−)
• At the same time two juridical CCCs cannot comprisecustomers that are lead members, i.e., are the holdings, of thesame company group.
A total of 30 Identification Constraints
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Constraints
EQLCs to impose complex business constraints
EQLC( verify not exists (
SELECT jurCCC.jccc
FROM sparqltable(SELECT ?jccc
WHERE{ ?jccc rdf:type ’JuridicalCCC’ })jurCCCWHERE jurCCC.jccc NOT IN (
SELECT withGroupLeader.jccc
FROM sparqltable(SELECT ?jccc, ?mem
WHERE{ ?cus rdf:type ’Customer’.
?cus :inMembership ?mem.?mem rdf:type ’Holding’.
?cus :inGrouping ?gr. ?gr :relativeTo ?jccc.
?jccc rdf:type ’JuridicalCCC’}) withGroupLeader ) ) )
• There does not exist a juridical CCC that does not comprise acustomer which is the holding member of a company group
A total of 27 Epistemic Constraint
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Constraints
EQLCs to impose complex business constraints
EQLC( verify not exists (
SELECT jurCCC.jccc
FROM sparqltable(SELECT ?jccc
WHERE{ ?jccc rdf:type ’JuridicalCCC’ })jurCCCWHERE jurCCC.jccc NOT IN (
SELECT withGroupLeader.jccc
FROM sparqltable(SELECT ?jccc, ?mem
WHERE{ ?cus rdf:type ’Customer’.
?cus :inMembership ?mem.?mem rdf:type ’Holding’.
?cus :inGrouping ?gr. ?gr :relativeTo ?jccc.
?jccc rdf:type ’JuridicalCCC’}) withGroupLeader ) ) )
• There does not exist a juridical CCC that does not comprise acustomer which is the holding member of a company group
A total of 27 Epistemic Constraint
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OBDA Mappings
The Data Source
• Currently, MPS applications managing CCCs rely over a 15million tuple database, stored in 12 relational tables underIBM DB2 RDBMS
Source name Source Description Source sizeGZ0001 Data on customers 3.463.083GZ0002 Data on juridical connections between customers 157.280GZ0003 Data on guarantee connection between customers 1.270.333GZ0004 Data on economical connections between customers 104.033GZ0005 Data on corporation connections between customers 1.021.779GZ0006 Data on patrimonial connections between customers 809.321GZ0007 Data on company groups 55.362GZ0012 Customers loan information 5.966.948GZ0015 Data on monitoring and reporting procedures 1.243GZ0101 Data on membership of customers into CCCs 2.225.466GZ0102 Information on CCCs 663.656GZ0104 Data on bank credit coordinators for juridical CCCs 38.457
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OBDA Mappings
OBDA Mappings: Example
SELECT id cluster, timestamp val FROM GZ0102, GZ0007
WHERE GZ0102.validity code = ‘T’ AND GZ0102.id cluster <> 0
AND GZ0007.validity code = ‘T’ AND GZ0007.id group <> 0
AND GZ0102.id cluster = GZ0007.id group
JuridicalCCC(ccc(id cluster, timestamp val)),timestamp(ccc(id cluster, timestamp val), timestamp val)
If the tuple (243, 24052009112341) is in ans(body) the we havethe following Virtual ABox assertions:
JuridicalCCC(gcc(243, 24052009112341))timestamp(gcc(243, 24052009112341)
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OBDA Mappings
OBDA Mappings: Example
SELECT id cluster, timestamp val FROM GZ0102, GZ0007
WHERE GZ0102.validity code = ‘T’ AND GZ0102.id cluster <> 0
AND GZ0007.validity code = ‘T’ AND GZ0007.id group <> 0
AND GZ0102.id cluster = GZ0007.id group
JuridicalCCC(ccc(id cluster, timestamp val)),timestamp(ccc(id cluster, timestamp val), timestamp val)
If the tuple (243, 24052009112341) is in ans(body) the we havethe following Virtual ABox assertions:
JuridicalCCC(gcc(243, 24052009112341))timestamp(gcc(243, 24052009112341)
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Experimentation Ontology usage
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Ontology usage
Verifying incompleteness in the data through queryanswering
Incompleteness of the data
Querying the database directly vs. querying the ontology providesmore answers.
• To retrieve the identification codes of all company groups.DB operations use id code from GZ0007
• Asking for q(y)← CompanyGroup(x), id code(x , y)
• Mastro indicates that GZ0007 is not the only relevant table.
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Ontology usage
Verifying inconsistencies in the data through queryanswering
Inconsistency of the data
Using epistemic query answering to locate inconsistent tuples.
• (functional ingrouping−)• We can detect the violating tuples using:
SELECT testview.l, testview.c1, testview.c2
FROM sparqltable (SELECT ?l ?c1 ?c2
WHERE{?c1:inGrouping?l. ?c2:inGrouping?l}) testview
WHERE testview.c1 <> testview.c2
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Query structure
Evaluation Performance
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Query structure
Query Performance
Query answering in DL-Lite for OBDA in a nutshell
• Reformulate w.r.t. T• Unfold w.r.t. M• Evaluate
Sources of complexity
• Reformulation - Size of the reformulation
• Unfolding - Size of the unfolding and query structure
Most critical aspect in the MPS scenario: query structure.
Mastro at Work Savo et. al.
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Query structure
Query Performance
Query answering in DL-Lite for OBDA in a nutshell
• Reformulate w.r.t. T• Unfold w.r.t. M• Evaluate
Sources of complexity
• Reformulation - Size of the reformulation
• Unfolding - Size of the unfolding and query structure
Most critical aspect in the MPS scenario: query structure.
Mastro at Work Savo et. al.
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Query structure
Query Performance
Query answering in DL-Lite for OBDA in a nutshell
• Reformulate w.r.t. T• Unfold w.r.t. M• Evaluate
Sources of complexity
• Reformulation - Size of the reformulation
• Unfolding - Size of the unfolding and query structure
Most critical aspect in the MPS scenario: query structure.
Mastro at Work Savo et. al.
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Query structure
Query Performance
Query answering in DL-Lite for OBDA in a nutshell
• Reformulate w.r.t. T• Unfold w.r.t. M• Evaluate
Sources of complexity
• Reformulation - Size of the reformulation
• Unfolding - Size of the unfolding and query structure
Most critical aspect in the MPS scenario: query structure.
Mastro at Work Savo et. al.
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Query structure
Query Structure
In Mastro, query unfolding is done by means of partial evaluationand SQL views.
Given a Virtual Abox defined by DB, the mappings M and a queryQ to be evaluated we:
• Define a set of auxiliary predicates and SQL views
• Associate these to T by means of a logic program P• Compute the partial evaluation of Q with respect to P• Translate the PE into SQL by means of the views.
Mastro at Work Savo et. al.
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Query structure
Query Structure
In Mastro, query unfolding is done by means of partial evaluationand SQL views.
Given a Virtual Abox defined by DB, the mappings M and a queryQ to be evaluated we:
• Define a set of auxiliary predicates and SQL views
• Associate these to T by means of a logic program P• Compute the partial evaluation of Q with respect to P• Translate the PE into SQL by means of the views.
Mastro at Work Savo et. al.
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Query structure
Query Structure
In Mastro, query unfolding is done by means of partial evaluationand SQL views.
Given a Virtual Abox defined by DB, the mappings M and a queryQ to be evaluated we:
• Define a set of auxiliary predicates and SQL views
• Associate these to T by means of a logic program P
• Compute the partial evaluation of Q with respect to P• Translate the PE into SQL by means of the views.
Mastro at Work Savo et. al.
![Page 32: DL'12 mastro at work](https://reader033.fdocuments.us/reader033/viewer/2022052819/54591422af7959795d8b547c/html5/thumbnails/32.jpg)
Query structure
Query Structure
In Mastro, query unfolding is done by means of partial evaluationand SQL views.
Given a Virtual Abox defined by DB, the mappings M and a queryQ to be evaluated we:
• Define a set of auxiliary predicates and SQL views
• Associate these to T by means of a logic program P• Compute the partial evaluation of Q with respect to P
• Translate the PE into SQL by means of the views.
Mastro at Work Savo et. al.
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Query structure
Query Structure
In Mastro, query unfolding is done by means of partial evaluationand SQL views.
Given a Virtual Abox defined by DB, the mappings M and a queryQ to be evaluated we:
• Define a set of auxiliary predicates and SQL views
• Associate these to T by means of a logic program P• Compute the partial evaluation of Q with respect to P• Translate the PE into SQL by means of the views.
Mastro at Work Savo et. al.
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Query structure
T -viewsExample:The mappings
m1: SELECT .... WHERE cd tp = 503 ; linkedTo(cus(idcus), link(linkid))m2: SELECT .... WHERE cd tp = 501 ; linkedTo(cus(idcus), link(linkid))
The view for AuxlinkedTo
SELECT ‘cus(’||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 503) view\_m1
UNION
SELECT ‘cus’(||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 501) view\_m2
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Query structure
T -viewsExample:The mappings
m1: SELECT .... WHERE cd tp = 503 ; linkedTo(cus(idcus), link(linkid))m2: SELECT .... WHERE cd tp = 501 ; linkedTo(cus(idcus), link(linkid))
The view for AuxlinkedTo
SELECT ‘cus(’||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 503) view\_m1
UNION
SELECT ‘cus’(||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 501) view\_m2
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Query structure
T -views, unfoldingProgram
linkedTo(x , y)← AuxlinkedTo(x , y)
The queryq(x , y)← linkedTo(x , z), linkedTo(y , z)
The partial evaluation
q(x , y)← AuxleadsTo(x , z),AuxlinkedTo(y , z)
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Query structure
T -views, unfolding
SELECT leadsto1.term1, leadsto2.term1 FROM (
SELECT ‘cus(’||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 503) view\_m1
UNION
SELECT ‘cus’(||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 501) view\_m2
) as leadsto1,
(
SELECT ‘cus(’||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 503) view\_m1
UNION
SELECT ‘cus’(||idcus||‘)’ as term1, ‘link(’||linkid||‘)’ as term2
FROM (SELECT .... WHERE cd\_tp = 501) view\_m2
) as leadsto2
WHERE leadsto1.term2 = leadsto2.term2
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Query structure
Performance of T -viewsPoor performance, in the order of hours, for trivial queries.
Culprit
Materialization of partial results in the DBMS query plans.
Solution
For relational DBMS queries, simpler is better.
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Query structure
Performance of T -viewsPoor performance, in the order of hours, for trivial queries.
Culprit
Materialization of partial results in the DBMS query plans.
Solution
For relational DBMS queries, simpler is better.
Mastro at Work Savo et. al.
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Query structure
Performance of T -viewsPoor performance, in the order of hours, for trivial queries.
Culprit
Materialization of partial results in the DBMS query plans.
Solution
For relational DBMS queries, simpler is better.
Mastro at Work Savo et. al.
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Query structure
M-views
Example:Mappings
m1: SELECT .... WHERE cd tp = 503 ; linkedTo(cus(idcus), link(linkid))m2: SELECT .... WHERE cd tp = 501 ; linkedTo(cus(idcus), link(linkid))
The views:
Auxm1 = SELECT .... WHERE cd tp = 503
Auxm2 = SELECT .... WHERE cd tp = 503
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Query structure
M-views, unfolding
Program:
linkedTo(cus(idcus), link(linkid))← Auxm1(idcus, linkid)
linkedTo(cus(idcus), link(linkid))← Auxm2(idcus, linkid)
The queryq(x , y)← linkedTo(x , z), linkedTo(y , z)
The partial evaluation
q(cus(idcus1), cus(idcus2))← Auxm1(idcus1, linkid1),Auxm1(idcus2, linkid1)
q(cus(idcus1), cus(idcus2))← Auxm1(idcus1, linkid1),Auxm2(idcus2, linkid1)
q(cus(idcus1), cus(idcus2))← Auxm2(idcus1, linkid1),Auxm2(idcus2, linkid1)
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Query structure
M-views, unfolding
Program:
linkedTo(cus(idcus), link(linkid))← Auxm1(idcus, linkid)
linkedTo(cus(idcus), link(linkid))← Auxm2(idcus, linkid)
The queryq(x , y)← linkedTo(x , z), linkedTo(y , z)
The partial evaluation
q(cus(idcus1), cus(idcus2))← Auxm1(idcus1, linkid1),Auxm1(idcus2, linkid1)
q(cus(idcus1), cus(idcus2))← Auxm1(idcus1, linkid1),Auxm2(idcus2, linkid1)
q(cus(idcus1), cus(idcus2))← Auxm2(idcus1, linkid1),Auxm2(idcus2, linkid1)
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Query structure
M-views, unfolding
Program:
linkedTo(cus(idcus), link(linkid))← Auxm1(idcus, linkid)
linkedTo(cus(idcus), link(linkid))← Auxm2(idcus, linkid)
The queryq(x , y)← linkedTo(x , z), linkedTo(y , z)
The partial evaluation
q(cus(idcus1), cus(idcus2))← Auxm1(idcus1, linkid1),Auxm1(idcus2, linkid1)
q(cus(idcus1), cus(idcus2))← Auxm1(idcus1, linkid1),Auxm2(idcus2, linkid1)
q(cus(idcus1), cus(idcus2))← Auxm2(idcus1, linkid1),Auxm2(idcus2, linkid1)
Mastro at Work Savo et. al.
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Query structure
M-views, unfolding
SELECT ’cus(’||auxm11.idcus||’)’ as x, ’cus(’||auxm12.idcus||’)’ as y
FROM (SELECT .... WHERE cd\_tp = 503) as auxm11,
(SELECT .... WHERE cd\_tp = 503) as auxm12
WHERE auxm11.linkid = auxm12.linkid
UNION
SELECT ’cus(’||auxm11.idcus||’)’ as x, ’cus(’||auxm21.idcus||’)’ as y
FROM (SELECT .... WHERE cd\_tp = 503) as auxm11,
(SELECT .... WHERE cd\_tp = 501) as auxm21
WHERE auxm11.linkid = auxm21.linkid
UNION
SELECT ’cus(’||auxm21.idcus||’)’ as x, ’cus(’||auxm22.idcus||’)’ as y
FROM (SELECT .... WHERE cd\_tp = 501) as auxm21,
(SELECT .... WHERE cd\_tp = 501) as auxm22
WHERE auxm21.linkid = auxm22.linkid
Mastro at Work Savo et. al.
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Query structure
Performance comparison
figures/performances4.pdf
Mastro at Work Savo et. al.
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Conclusions
Conclusions
Mastro at Work Savo et. al.
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Conclusions
MPS feedback
Useful result from the MPS point of view
• Data Integration
• Data Quality
• Knowledge Sharing
From the technical point of view:
• DBMS level performance for on-the-fly OBDA is possible
• Query tuning is mandatory.
• Pinpointed the features of the queries that are needed forgood performance and those that trigger bad performance.
Mastro at Work Savo et. al.
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Conclusions
Current and Future work
• Experiment with live access to the sources
• Extend the current experimentation to other data domains inMPS
Preview of the Mastro OBDA plugin and the OBDA plugin forProtege 4.0
• http://www.dis.uniroma1.it/quonto/
• http://obda.inf.unibz.it
Mastro at Work Savo et. al.