Guy Pierra

Decomposition through formalization in a product space

When an optimization problem is posed in a product space it is classical to decompose this problem. The goal of this paper is to show how such an approach can be used when the problem to be solved is not naturally posed in a product space. By associating systematically to this problem an equivalent one posed in ann-fold cartesian product space, we obtain by decomposition of the latter both a splitting of operators and a desintegration of constraints for the former. Applications to three rather classical mathematical programming problems are given.

Contribution of ontology-based data modeling to automatic integration of electronic catalogues within engineering databases

Developing intelligent systems to integrate numerous, autonomous and heterogeneous data sources in order to give end users an uniform query interface is a great challenging issue. The process of constructing a global schema of the integrated system is usually done manually. This is due to the presence of semantic and schematic heterogeneities among schemas of sources. In most cases, sources do not contain enough knowledge to help in solving these heterogeneities and then generating the global schema. In this paper, we present an ontology-driven integration approach called a priori approach. Its originality is that each data source participating in the integration process contains an ontology that defines the meaning of its own data. This approach ensures the automation of the integration process when all sources reference a shared ontology, and possibly extend it by adding their own concept specializations. We present two integration algorithms where (1) the shared ontology may be extended during the integration process, and (2) the instances of local sources are projected onto the shared ontology. Finally, we show that this theory allows to integrate automatically electronic catalogues into corporate engineering databases using the PLIB ontology model.

OntoDB: an ontology-based database for data intensive applications

Recently, several approaches and systems were proposed to store in the same database data and the ontologies describing their meanings. We call these databases, ontology-based databases (OBDBs). Ontology-based data denotes those data that represent ontology individuals (i.e., instance of ontology classes). To speed up query execution on the top of these OBDBs, efficient representations of ontology-based data become a new challenge. Two main representation schemes have been proposed for ontology-based data: vertical and binary representations with a variant called hybrid. In these schemes, each instance is split into a number of tuples. In this paper, we propose a new representation of ontology-based data, called table per class. It consists in associating a table to each ontology class, where all property values of a class instance are represented in a same row. Columns of this table represent those properties of the ontology class that are associated with a value for at least one instance of this class. We present the architecture of our ontology-based databases and a comparison of the effectiveness of our representation scheme with the existing ones used in Semantic Web applications. Our benchmark involves three categories of queries: (1) targeted class queries, where users know the classes they are querying, (2) no targeted class queries, where users do not know the class(es) they are querying, and (3) update queries.

Domain Ontologies: A Database-Oriented Analysis

If the word ontology is more and more used in a number of domain, the capabilities and benefits of ontology for Information Systems management are still unclear. Therefore, the usage of ontology-based Information Systems in industry and services is not widespread. This paper analyses the concept of a domain ontology from a database perspective. As a result, firstly, we provide three criteria that distinguish domain ontology from other existing domain modeling approach which lead us to propose a new definition of domain ontologies. Secondly, based on the various approaches of ontology modeling followed by different communities, we propose a taxonomy of domain ontology. We show how they may be organized into a layered model, called the onion model, allowing to design and to use the capabilities of each category of ontology in an integrated environment. Finally, this paper presents several information systems based on ontology technologies and describe the kinds of services that should be provided to allow a powerful usage of ontology in data management.

From digital libraries to electronic catalogues for engineering and manufacturing

Over the last decade, a formal data model of libraries of parts for manufacturing and engineering has been developed. This model, known as PLIB, officially ISO 13584, is suitable not only for the exchange of files containing parts, independent of any application that is using these files, but also as a basis for implementing and sharing databases of parts library data. More recently, a strong requirement emerged for exchanging also electronic catalogues containing parts library data. This paper proposes an approach for integrating the data model-oriented view and the document oriented view on the same information, namely parts definition and representation information. This integration embeds three levels. At the modelling level, rules are defined for deriving an XML document type definition from an information model in the EXPRESS language. At the implementation level, an approach is defined for integrating authoring tools, based on XML, and semantic checkers, based on EXPRESS. At the data file level, the proposed integration results in a concept of semantic document that is both human readable and semantically computer-sensible. Application of this approach for capturing parts library data and for generating active documents is presented.

A versioning management model for ontology-based data warehouses

More and more integration systems use ontologies to solve the problem of semantic heterogeneities between autonomous databases. To automate the integration process, a number of these systems suppose the existence of a shared domain ontology a priori referenced by the local ontologies embedded in the various sources. When the shared ontology evolves over the time, the evolution may concern (i) the ontology level, (2) the local schema level, and/or (3) the contents of sources. Since sources are autonomous and may evolve independently, managing the evolution of the integrated system turns to an asynchronous versioning problem. In this paper, we propose an approach and a model to deal with this problem in the context of a materialized integration system. To manage the changes of contents and schemas of sources, we adapt the existing solutions proposed in traditional databases. To support ontology changes, we propose the principle of ontological continuity. It supposes that an evolution of an ontology should not make false an axiom that was previously true. This principle allows the management of each old instance using the new version of ontology. With this assumption, we propose an approach, called the floating version model, that fully automate the whole integration process. Our proposed work has been validated by a prototype using ECCO environment and the EXPRESS language.

An a Priori Approach for Automatic Integration of Heterogeneous and Autonomous Databases

Data integration is the process that gives users access to multiple data sources through queries against a global schema. The semantic heterogeneity has been identified as the most important and toughest problem when integrating various sources. The mapping between the global schema and local schemas was done manually in the first generation of integrated systems, when ontologies are not used to make explicit data meaning. It is semi automatic when ontologies and ontology mappings are defined at integration level. In this paper, we propose a fully automatic integration approach based on ontologies. It supposes that each data source contains a formal ontology that references a shared ontology. The relationships between each local ontology and the shared ontology are defined at the database design time and also embedded in each source. We assume that a domain ontology exists, but each source may extend it by adding new concepts and properties. Our approach is currently prototyped in various environments: OODB, ORDB, and RDB.

OntoDB: it is time to embed your domain ontology in your database

This demonstration presents OntoDB, a prototype that allows to store explicitly in the database not only the data, but also the conceptual model defining the structure of data and the domain ontology representing the meaning of data. The demonstration illustrates three main functionalities of OntoDB: (1) a storage of a domain ontology and database content in the same repository, (2) the possibility of querying databases at ontology level, and (3) an automatic integration of heterogeneous data sources referencing/extending the same domain ontology.

Bases de données à base ontologique. Principe et mise en oeuvre

In this paper, we propose a new database model called Ontology-Based Database (OBDB) that has two main characteristics. First, it allows to manage both ontologies and data. Second, it permits to associate each data with the ontological concept which defines its meaning. Our approach addresses ontologies expressed as models, i.e. sets of objects that may be accessed from a root object, and that consists of classes and properties. Target data are those data that are instances of ontology classes and that fulfil a strong typing assumption: each instance shall belong to exactly one base class, defined as the lower bound (for the subsumption relationship) of the classes the instance belongs to, and it shall be described only by properties that are applicable to its base class. We first propose an OBDB architecture model, called OntoDB. Then we outline its implementation using the PostgreSQL DBMS, the PLIB ontology model and the EXPRESS language.

SISRO, conception de bases de données à partir d'ontologies de domaine

Database design methodologies require both good modelling capabilities and knowledge of the field to be modelled. The first aspect is a one of the major difficulties for designers, since they have to move from one field to another one. Moreover, resulting databases are heterogeneous and difficult to integrate. In this paper, we propose a database design methodology, called SISRO. It is based on a prior definition of a local ontology for the database to be designed. This ontology is defined by specialization of shared domain ontologies, selective import of properties, and eventually extension with necessary concepts and/ or properties. The conceptual model is defined as a fragment of local ontology. Representing both ontologies and their articulations in the resulting database provides easy access, exchange and integration of data.