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A RDF-based Data Integration Framework

Amineh Amini

, Hadi Saboohi

, Nasser Nematbakhsh

1,2

Department of Computer Engineering,

Islamic Azad University, Karaj Branch, Karaj, Iran

{aamini, saboohi} @kiau.ac.ir

Department of Computer Engineering,

Faculty of Engineering, University of Isfahan, Isfahan, Iran

nemat@eng.ui.ac.ir

Abstract

Data integration is one of the main problems in distributed data sources. An approach is to provide an

integrated mediated schema for various data sources. This research work aims at developing a framework for

defining an integrated schema and querying on it. The basic idea is to employ recent standard languages and

tools to provide a unified data integration framework. RDF is used for integrated schema descriptions as well as

providing a unified view of data. RDQL is used for query reformulation. Furthermore, description logic

inference services provide necessary means for satisfiability checking of concepts in integrated schema. The

framework has tools to display integrated schema, query on it, and provides enough flexibilities to be used in

different application domains.

Keywords

Data Integration, Semantic Web, RDF, RDQL, Query Reformulation, Description Logic (DL)

1. Introduction

Data integration is the problem of combining the data

residing at different, heterogeneous sources and providing

the user with a unified view of this data, called mediated

schema, which can be queried by users. A data

integration system frees the user from the knowledge of

where and how data are represented in the sources. The

interest in this kind of systems has been continuously

increased in recent years by the fact that many

organizations face the problem of integrating data

residing in several different sources. Specifically

companies that build data warehouses, doing data mining

and developing Enterprise Resource Planning systems

must address this problem. Also, integrating data in the

World Wide Web is a subject of several investigations

and projects nowadays [1]. Using Semantic Web

concepts and services for data integration brings new

possibilities and challenges. For example adding semantic

to the data makes them processable by computer agents.

Also it is possible to check for consistency of concepts in

the integrated schema.

Early works on integration were carried out in the

context of database design, and focused on the so-called

schema integration problem, i.e. designing a global,

unified schema for a database application starting from

several sub schemata, each one produced independently

from the others [2]. More recent efforts have been

devoted to data integration, which generalizes schema

integration by taking into account actual data in the

integration process. Here the input is a collection of

source data sets (each one constituted by a schema and

actual data), and the goal is to provide an integrated and

reconciled view of the data residing at the sources,

without interfering with their autonomy [3]. We only deal

with the so-called read-only integration, which means

that such a reconciled view is used for answering queries,

and not for updating information.

Data integration can be either virtual or materialized.

In the first case, the integration system acts as an

interface between the user and the sources [4], and is

typical of multi-databases, distributed databases, and

more generally open systems. In virtual integration, query

answering is generally costly, because it requires

accessing the sources. In the second case, the system

maintains a replicated view of the data at the sources [5],

and is typical, for example, both in information system

re-engineering and data warehousing. In materialized data

integration, query answering is generally more efficient,

because it does not require accessing the sources, whereas

maintaining the materialized views is costly, especially

when the views must be up-to-date with respect to the

updates at the sources (view refreshment). In the rest of

this paper, we do not deal with the problem of view

refreshment.

There are two basic approaches to the data integration

problem, called procedural and declarative. In the

procedural approach, data are integrated in an ad-hoc

manner with respect to a set of predefined information

needs. In this case, the basic issue is to design suitable

software modules that access the sources in order to

fulfill the predefined information requirements. Several

data integration (both virtual and materialized) projects,

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