Bernadette Farias Lóscio

Feedback-based data set recommendation for building linked data applications

The huge and growing volume of linked data is increasing the interest in developing applications on top of such data. One of the distinguishing features of linked data applications is that the data could come from any RDF data set available on the Web. Different from conventional applications, where the data sources are under control of the applications owner or developer, linked data applications follow the Semantic Web vision of a world full of reusable data. Considering a potentially large number of data sets, one of the primary challenges facing the development of such solutions is the identification of suitable data sources, i.e., data sets that could give a good contribution to the answer of user queries. In this paper, we discuss this problem and we present a feedback-based approach to incrementally identify new data sets for domain-specific linked data application.

Managing the Evolution of Mediation Queries

Previous works in data integration can be classified according to the approach used to define objects at the mediation level. One of these approaches is called global-as-view (GAV) and requires that each object is expressed as a view (a mediation query) on the data sources. One important limit of this approach is the management of the evolutions in the system. Indeed, each time a change occurs at the source schema level, all the queries defining the mediation objects have to be reconsidered and possibly redefined. In this paper, we propose an approach to cope with the evolution of mediation queries. Our claim is that if the definition of mediation queries in a GAV context follows a well-defined methodology, handling the evolution of the system becomes easier. These evolution problems are considered in the context of a methodology we have previously defined for generating mediation queries. Our solution is based on the concept of relevant relations on which propagation rules have been defined.

Open Government Data Portals Analysis: The Brazilian Case

Governments and public agencies are publishing data on the Web, called Open Government Data (OGD), without restrictions for its sharing and reuse. This growing interest has motivated Brazilian Government Administrations to launch Open Data initiatives. As a consequence, in the last years, several Brazilian Open Data portals have been launched. Nowadays, there is a huge number of OGD portals from different countries and a lot of effort has been made to evaluate them. Although Brazil is one of the leaders of the Open Data initiative in South American and one of the founders of the Open Government Partnership, there are no comprehensive reports about the current Brazilian OGD scenario. In this context, we evaluated 13 Brazilian OGD portals according to several criteria ranging from the volume of data available to the quality of the data. As part of the results, we found that quantity and size of datasets are very diverse, CSV is the most used data format, at least 47 different data formats have been used to publish Open Data and most of the Brazilian Open Data portals use the CKAN platform. In this paper, we present and discuss the research method and the results of our work.

Towards Ecosystems based on Open Data as a Service

Despite several efforts in contests throughout the world that encourage local communities to develop applications based on government Open Data, the solutions resulting from such initiatives do not have longevity, lacking maintenance and rapidly falling into disuse. This is due mainly to the lack of investment or even a model for monetizing the use of such applications. Therefore, it is necessary to develop a model that fosters the value chain for Open Data aiming an economically self-sustained ecosystem. Such ecosystem should promote new businesses through the creation of systems and applications focused on citizens. This article discusses the creation of software ecosystems for services and applications underpinned by a platform based on Open Data as a Service.

Evolution of XML-Based Mediation Queries in a Data Integration System

One of the main challenges in data integration systems is the maintenance of the mappings between the mediation schema and the source schemas. In a dynamic environment, such mappings must be flexible enough in order to accommodate new data sources and new users’ requirements. In this context, we address a novel and complex problem that consists in propagating a change event occurring at the source level or at the user level into the mediation level. We propose an incremental approach to develop the mediation schema and the mediation queries based on the evolution of the data source schemas and the evolution of the users’ requirements. The proposed approach allows the mediation level to evolve incrementally and modifications can be handled easier increasing the system flexibility and scalability.

Using Heterogeneous Mappings for Rewriting SPARQL Queries

In this paper we present an approach for rewriting queries between heterogeneous ontologies. In our query rewriting solution, we combine the semantics and expressiveness of SPARQL with notions of logic programming and adopt a rule-based formalism for representing mappings between ontologies. We also deal with some relevant questions, including: structural heterogeneities and pruning of irrelevant parts of a rewritten query.

Query reformulation for an XML-based data integration system

The main objective of a mediator-based data integration system is to provide a unified view of several distributed and heterogeneous data sources. This view, called mediation schema, corresponds to a set of elements computed from data available on the local data sources. One of the biggest challenges facing a data integration system consists in answering a user query submitted in terms of the mediation schema given that the data is at the sources. This problem consists mainly in reformulating the query in terms of the source schemas and in integrating the corresponding answers. In this paper we address the problem of query reformulation in the context of an XML-based data integration system. Our solution is based on algorithms that generate a set of source queries considering a given user query and a set of mappings between the mediation schema and the data source schemas.

Heterogeneous Data Source Integration and Evolution

Data integration consists in providing a uniform view of a set of heterogeneous data sources. This allows users to define their queries without any knowledge on the heterogeneous sources. Data integration systems use the mediation architecture to provide integrated access to multiple data sources. A mediator is a software device that supports a mediation schema which captures user requirements, and a set of mappings between the mediation schema and the distributed sources. Mediation systems [20] can be classified according to the approach used to define the mappings between the data sources and the global schema [12,23]. The first approach is called global-asview (GAV) and requires that each object of the global schema be expressed as a view (i.e. a query) on the data sources [6]. In the other approach, called local-as-view (LAV), mediation mappings are defined in an opposite way; each object in a given source is defined as a view on the global schema [11]. Both approaches allow to transform user queries, defined over the mediation schema, into subqueries defined over data sources. The transformation process is called a rewriting process and is done differently, depending on the approach used.

Using information quality for the identification of relevant web data sources: a proposal

In the last decade, applications that make use of data sources available on the Web have experienced a huge growth. One of the main problems regarding that consists in finding the most relevant data sources for a given application. In a general way, a data source is considered relevant when it contributes for answering queries submitted to the application. However, it may happen that a specific data source contributes for answering an application query but the answer provided by the data source does not really meet the user requirements. This may occur because the data source has generic data and the user wants more specific data, for example. On the other hand, some data sources may have data of poor quality, i.e., the data may be outdated, incomplete or incorrect. In such cases, it is not enough just to find data sources that can answer to the application queries. It is also important to check if the available data also meet the user needs. In this paper, we discuss such problem and we propose an approach, based on Information Quality (IQ), to help the evaluation of the relevance of a Web data source for domain-specific applications. We also present an example illustrating how our proposal can be used to enhance this evaluation.

MeMO: A Clustering-based Approach for Merging Multiple Ontologies

Since numerous ontologies are available on the Web, the requirement for merging such ontologies remains a pertinent issue in several applications. Many solutions were proposed in the literature to solve the ontology merging problem. However, these solutions just deal with the combination of two source ontologies at a time. To address the challenge of automatically merging multiple source ontologies, we propose a clustering-based approach. In our approach, named MeMO, the similarity among the source ontologies is calculated with the aim of defining the order in which they will be merged. A distinguishing point of our proposal is that we consider that better results are obtained when more similar ontologies are combined in the first place. We argue that the combination of ontologies with low level of similarity can introduce mistakes that will be carried out during the whole merging process. This argument is demonstrated in our experimental evaluation.