1. the Context-ADDICT
project
Ontology driven,
context-aware query distribution
for on-the-fly data-integration
Letizia Tanca and Giorgio Orsi

2. Data Integration: State of the art
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

3. …the future
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

4. 4
Overview
An ontology-driven solution for dynamic data
integration, within a scenario where:

data sources are not known a-priori

user queries are dealt with in a context-aware fashion

information fruition is fostered by

handing it to the user in a semantics-aware, integrated fashion

eliminating non-interesting information, thus reducing the
“information noise”

controlling the problem’s dimension via context-based reduction of
the current information space
We propose a DL language, CA-DL, which can uniformly
represent the application domain and the context
Queries are issued to the system in SPARQL and
translated into CA-DL for internal processing
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

5. Context-ADDICT
(joint work with C. Bolchini, E. Quintarelli and F. A. Schreiber)
Features
 Context-aware data/ontology tailoring [5]
 Ontology-driven, on-the-fly data integration of heterogeneous and dynamic
data sources
 Multimodal access to resources
 Focus on small and mobile devices (sensors, mobile phones, custom
embedded-systems)
Applications
 Urban mobility
 Automotive,
 e-Health
 Logistics
 Energy
 Production Automation
 Automated and Personalized Advertisement
 Personal Information Systems
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

6. Context-ADDICT : context-aware integration of the 6
overall information collected from the data sources
[MDM06]
On-the-fly data integration + data reduction via tailoring
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

7. 7
Modeling context: the CDT
• An orthogonal context model, which can be adopted for any
application (data tailoring, application and service adaptivity and
fine-tuning, sensor queries…)
• Single contexts are defined as subtrees of a Context Tree,
representing the contexts currently envisaged for that particular
application
• Fine granularity, semantics- based …
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

8. Domain Ontology
Domain Ontology:
• Supplies to the absence of a DB “global schema”
• Shared and commonly agreed
• Must be decidable and efficiently computable  CA-DL
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

9. Data Sources: Semantic Extraction
Data Source Ontology:
• Semantic Extraction: semantic ontology + structural ontology
• Models structural/semantic independence (the different models
can be used separately)
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

10. CDT  domain ontology  source ontologies
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

11. Relevant areas, or projections
Projection:
• is the set of relevant data for a given user in a given context
• projected from the ADO to the data sources
• is context-aware
• possibly materialized on the user device
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

12. Our problem
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

13. A closer look
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

14. CA-DL
CA-DL is used to create mappings between data sources and
application domain ontologies and to represent the application
context.
CA-DL corresponds to a strict subset of OWL2, tailored to be rewritable
from/to SPARQL syntax and to express both GAV and LAV
mappings.
A SPARQL query is issued to the system, and:
• translated into CA-DL
• transformed by adapting it to the current user context
• handed over to the query-rewriting algorithm(s) which distribute it to the
suitable data sources (i.e. when alternative data-sources are available)
• translated into the data-source language(s) by means of automatically
generated wrappers
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

15. In CA-DL
No unions, keeping the complexity of the rewriting process within
PTIME, and only allowing LAV mappings which involve intersections
of concepts: in a CA-DIS the queries are highly heterogeneous and
the mappings are often computed on-the-fly.
No universal quantification: because GAV mappings rewrite the
complex mapping into SPARQL syntax, where currently it is not
possible to express general universal restrictions. Only special form of
universal restriction: property range definitions where
the concept N is the range of the property R.
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

16. The CDT for the insurance company
application
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Dipartimento di Elettronica e Informazione

17. The CDT ontology
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18. The application domain ontology
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Dipartimento di Elettronica e Informazione

19. A context and its relevant area
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Dipartimento di Elettronica e Informazione

20. The application domain ontology
manufacturer
haspolicy expectsreceipt
hasBrand Mname
policy
vehicle hasName customer receipt
man hasclaim envisages
hasriskclass
motorcycle driver risk
car woman payment
Haspayment
drives
high low
claim
mid
Relevant area
for context c1
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

21. The data sources and their semantic ontologies
DS1: Customer(id, name, ownesMotorbikePlateNumber)
Motorbike(motorbikePlateNumber, manufacturer, model)
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

22. The data sources and their semantic ontologies
DS2:
Client(id, fullName, riskClass, gender)
RiskClass(id, description)
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

23. The mapping ontology
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Dipartimento di Elettronica e Informazione

24. Context-aware queries for context c1
q(x,w)  Customer(x), drives(x, y), hasBrand(y, z), hasMname(z, w)
This query correctly retrieves all the customers who drive a car with
their manufacturer’s names, since the requested concepts and
roles are included in the relevant area for context c1
q(x,y)  Customer(x), hasName(x, y)
This query correctly retrieves all the customers with their names,
since the requested concept and property are included in the
relevant area for context c1
q(x,z)  Customer(x), hasPolicy(x, y), envisages(y, z)
The answer to his query is empty in context c1, since its relevant
area does not include the roles hasPolicy and envisages
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

25. Context-aware queries: Context c1
q(x,y)  Customer(x), hasName(x,y)
• The query is distributed to the datasources D1 and D2, after a
reasoning step, through the mapping ontology.
• The concept DS1:Customer is mapped (via LAV mappings) to an
anonymous concept of the domain ontology containing women
who drive motorbikes. The data property ado:hasName is
mapped to the data property DS1:name
• The concept ado:Customer is mapped (via GAV mapping) to and
to an anonymous concept containing DS2:Client who has male
gender with high risk class. The data property ado:hasName is
mapped to the dataproperty DS2:fullname
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

26. The data sources and their semantic ontologies
DS1: Customer(id, name, ownesMotorbikePlateNumber)
Motorbike(motorbikePlateNumber, manufacturer, model)
SELECT id, name
FROM Customer
Note: the customers here are only women !!
DS2:
Client(id, fullName, riskClass, gender)
RiskClass(id, description)
SELECT id, fullname
FROM Client, RiskClass
WHERE Client.riskClass=RiskClass.id
AND RiskClass=“high”
AND gender=“male”
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

27. Conclusions and future work
An ontology-driven solution for dynamic
data integration, where:
 data sources are not known a-priori
 user queries are dealt with in a context-aware fashion
The future:
 Performance evaluation, in terms of:
• Recall/precision
• Efficiency
 Usage of the same framework in an Internet of things scenario
the Context-ADDICT project
Dipartimento di Elettronica e Informazione

28. Some references …
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Dipartimento di Elettronica e Informazione

29. CA-DL axioms
the Context-ADDICT project
Dipartimento di Elettronica e Informazione