Ontology driven, Ontology driven, context-aware query distribution context-aware query distribution for on-the-fly data-integration for on-the-fly data-integration Letizia Tanca and Giorgio Orsi Letizia Tanca and Giorgio Orsi the Context-ADDICT the Context-ADDICT project project
1. the Context-ADDICT projectOntology driven,context-aware
query distributionfor on-the-fly data-integrationLetizia 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. 4OverviewAn 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
problems dimension via context-based reduction of the current
information spaceWe propose a DL language, CA-DL, which can
uniformly represent the application domain and the contextQueries
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 6overall
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 OntologyDomain 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 projectionsProjection: 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-DLCA-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-DLNo 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 companyapplication the
Context-ADDICT project Dipartimento di Elettronica e
Informazione
17. The CDT ontology the Context-ADDICT project Dipartimento di
Elettronica e Informazione
18. The application domain ontology the Context-ADDICT project
Dipartimento di Elettronica e Informazione
19. A context and its relevant area the Context-ADDICT project
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
riskcar 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 ontologiesDS1:
Customer(id, name, ownesMotorbikePlateNumber)
Motorbike(motorbikePlateNumber, manufacturer, model) the
Context-ADDICT project Dipartimento di Elettronica e
Informazione
22. The data sources and their semantic
ontologiesDS2:Client(id, fullName, riskClass, gender)RiskClass(id,
description) the Context-ADDICT project Dipartimento di Elettronica
e Informazione
23. The mapping ontology the Context-ADDICT project
Dipartimento di Elettronica e Informazione
24. Context-aware queries for context c1q(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
manufacturers names, since the requested concepts and roles are
included in the relevant area for context c1q(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 c1q(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 ontologiesDS1:
Customer(id, name, ownesMotorbikePlateNumber)
Motorbike(motorbikePlateNumber, manufacturer, model) SELECT id,
name FROM CustomerNote: 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 workAn ontology-driven solution for
dynamic data integration, where: data sources are not known
a-priori user queries are dealt with in a context-aware fashionThe
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 the Context-ADDICT project Dipartimento di
Elettronica e Informazione
29. CA-DL axioms the Context-ADDICT project Dipartimento di
Elettronica e Informazione