Data Extraction, Transformation and Integration Guided by an Ontology - Data Warehousing Design and Advanced Engineering Applications - page 19

Figure 1. Functional architecture

We advocate an information integration approach
supporting the acquisition of data from a set of
external sources available for an application of
interest. This problem is a central issue in several
contexts, data warehousing, interoperate systems,
multi-database systems, web information systems.
Several steps are required for the acquisition of
data from a variety of sources to a data warehouse
based on an ontology (1) Data extraction: only
data corresponding to descriptions in the ontology
are relevant. (2) Data transformation: they must
be defined in terms of the ontology and in the
same format. (3) Data integration and reconcili-
ation: the goal of this task is to resolve possible
redundancies.
As a vast majority of sources rely on XML,
an important goal is to facilitate the integration
of heterogeneous XML data sources. Further-
more, most applications based on the Semantic
Web technologies rely on RDF (McBride, 2004),
OWL-DL (Mc Guinness & Van Harmelen, 2004)
and SWRL (Horrocks et al., 2004). Solutions for
data extraction, transformation and integration
using these recent proposals must be favoured.
Our work takes place in this setting. We propose
an integration middleware which extracts data
from external XML sources that are relevant ac-
cording to a RDFS+ ontology (RDFS+ is based
on RDFS (McBride, 2004)), transforms them
into RDF facts conformed to the ontology, and
reconciles redundant RDF data.

Our approach has been designed in the set-
ting of the PICSEL3 projecti i whose aim was to
build an information server integrating external
sources with a mediator-based architecture and
data originated from external sources in a data
warehouse. Answers to users' queries should be
delivered from the data warehouse. So data have
to be passed from (XML) external sources to the
(RDF) data warehouse and answers to queries
collected from external sources have to be stored
in the data warehouse. The proposed approach has
to be totally integrated to the PICSEL mediator-
based approach. It has to be simple and fast in
order to deal with new sources and new content
of integrated sources. Finally, it has to be generic,
applicable to any XML information source relative
to any application domain. In Figure 1 we present
the software components designed in the setting
of the project to integrate sources and data. This
paper focuses on the description of the content
of a source, the extraction and the integration of
data (grey rectangles in Figure 1). The automatic
generation of mappings is out of the scope of the
paper.
The extraction and transformation steps rely
on correspondences or mappings between local
schemas of external sources and the ontology. In
a previous work, we proposed techniques to auto-
mate the generation of these mappings (Reynaud
& Safar, 2009). In this chapter, we present an
approach which automates the construction of