Recently, schema matching has found considerable interest in both research and practice. Determining matching components of database or XML schemas is needed in many applications, e.g. for E-business and data integration. Various schema matching systems have been developed to solve the problem semi-automatically. While there have been some evaluations, the overall effectiveness of currently available automatic schema matching systems is largely unclear. This is because the evaluations were conducted in diverse ways making it difficult to assess the effectiveness of each single system, let alone to compare their effectiveness. In this paper we survey recently published schema matching evaluations. For this purpose, we introduce the major criteria that influence the effectiveness of a schema matching approach and use these criteria to compare the various systems. Based on our observations, we discuss the requirements for future match implementations and evaluations.

Government initiatives for more transparency and participation have lead to an increasing amount of structured data on the web in recent years. Many of these datasets have great potential. For example, a situational analysis and meaningful visualization of the data can assist in pointing out social or economic issues and raising people’s awareness. Unfortunately, the ad-hoc analysis of this so-called Open Data can prove very complex and time-consuming, partly due to a lack of efficient system support.On the one hand, search functionality is required to identify relevant datasets. Common document retrieval techniques used in web search, however, are not optimized for Open Data and do not address the semantic ambiguity inherent in it. On the other hand, semantic integration is necessary to perform analysis tasks across multiple datasets. To do so in an ad-hoc fashion, however, requires more flexibility and easier integration than most data integration systems provide. It is apparent that an optimal management system for Open Data must combine aspects from both classic approaches. In this article, we propose OPEN, a novel concept for the management and situational analysis of Open Data within a single system. In our approach, we extend a classic database management system, adding support for the identification and dynamic integration of public datasets. As most web users lack the experience and training required to formulate structured queries in a DBMS, we add support for non-expert users to our system, for example though keyword queries. Furthermore, we address the challenge of indexing Open Data.

Les travaux que nous présentons dans cette thèse concernent l’automatisation de la construction de systèmes médiateurs pour le web sémantique. L’intégration de données de manière générale et la médiation en particulier sont des processus qui visent à exploiter conjointement des sources d’information indépendantes, hétérogènes et distribuées. L’objectif final est de permettre à un utilisateur d’interroger ces données comme si elles provenaient d’un système unique et centralisé grâce à une interface d’interrogation uniforme basée sur un modèle du domaine d’application, appelé schéma global. Durant ces dernières années, beaucoup de projets de recherche se sont intéressés à cette problématique et de nombreux systèmes d’intégration ont été proposés. Cependant, la quantité d’intervention humaine nécessaire pour construire ces systèmes est beaucoup trop importante pour qu’il soit envisageable de les mettre en place dans bien des situations. De plus, face à la diversité et à l’évolution croissante des sources d’information apparaissent de nouveaux chalenges relatifs notamment à la flexibilité et à la rapidité d’accès à l’information. Nos propositions s’appuient sur les modèles et technologies du web sémantique. Cette généralisation du web qui est un vaste espace d’échange de ressources, non seulement entre êtres humains, mais également entre machines, offre par essence les moyens d’une automatisation des processus d’intégration. Ils reposent d’une part sur des langages et une infrastructure dont l’objectif est d’enrichir le web d’informations "sémantiques", et d’autre part sur des usages collaboratifs qui produisent des ressources ontologiques pertinentes et réutilisables. / This thesis is set in a research effort that aims to bring more automation in building mediator-based data integration systems in the semantic Web context. The mediator approach is a conceptual architecture of data integration that involves combining data residing in different sources and providing users with a unified view of these data. The problem of designing effective data integration solutions has been addressed by several researches, and well-known data integration projects have been developed during the 90’s. However, the building process of these systems rely heavily on human intervention so that it is difficult to implement them in many situations. Moreover, faced with the diversity and the increase of available information sources, the easiness and fastness of information access are new challenges. Our proposals are based on models and technologies of semantic web. The semantic web is recognized as a generalization of the current web which enables to find, combine and share resources, not only between humans but also between machines. It provides a good track for automating the integration process. Possibilities offered by the semantic web are based, on the one hand, on languages and an infrastructure aiming to enrich the web with "semantic" information and, on the other hand, on collaborative practices that allow the production of relevant and reusable ontological resources.