Many institutions publish datasets as Open Data in catalogs, however, their retrieval remains problematic issue due to the absence of dataset search benchmarking. We propose a framework for evaluating findability of datasets, regardless of retrieval models used. As task-agnostic labeling of datasets by ground truth turns out to be infeasible in the general domain of open data datasets, the proposed framework is based on evaluation of entire retrieval scenarios that mimic complex retrieval tasks. In addition to the framework we present a proof of concept specification and evaluation on several similarity-based retrieval models and several dataset discovery scenarios within a catalog, using our experimental evaluation tool. Instead of traditional matching of query with metadata of all the datasets, in similarity-based retrieval the query is formulated using a set of datasets (query by example) and the most similar datasets to the query set are retrieved from the catalog as a result.

There is a vast amount of datasets available as Open Data on the Web. However, it is challenging for consumers to find datasets relevant to their goals. This is because the available metadata in catalogs is not descriptive enough. Nevertheless, datasets exist in various types of contexts not expressed in the metadata. These may include information about the data publisher, the legislation related to dataset publication, etc. In this paper we describe an idea of a data model that enables consumers to better understand the data. We propose to define a formal model for representation of the datasets and their contexts, and we propose to apply existing similarity techniques, adjust them to fit each identified dataset context type and combine them together to measure similarity of datasets in new ways, improving their findability.

While the number of Linked Data (LD) datasets grows, the support for their consumption is still quite limited. Data publishers expect high reuse of their LD in different applications. At the same time, their users expect that the applications will be reusable for different LD datasets. However, the reality is different because existing LD often use proprietary vocabularies and unique combinations of nonproprietary ones. In this paper, we introduce a backend of a platform which interconnects LD datasets on one side with applications for previewing the datasets on the other. The platform backend is automatically discovers so called application pipelines for a given set of datasets. Each application pipeline specifies a sequence of transformation steps which transform the original data to a form required by an application. The platform is also able to execute a chosen pipeline and provide the result to the application in a prepared SPARQL endpoint.

As the size of semantic data available as Linked Open Data (LOD) increases, the demand for methods for automated exploration of data sets grows as well. A data consumer needs to search for data sets meeting his interest and look into them using suitable visualization techniques to check whether the data sets are useful or not. In the recent years, particular advances have been made in the field, e.g., automated ontology matching techniques or LOD visualization platforms. However, an integrated approach to LOD exploration is still missing. On the scale of the whole web, the current approaches allow a user to discover data sets using keywords or manually through large data catalogs. Existing visualization techniques presume that a data set is of an expected type and structure. The aim of this position paper is to show the need for time and space efficient techniques for discovery of previously unknown LOD data sets on the base of a consumer’s interest and their automated visualization which we address in our ongoing work

Recently, the amount of semantic data available in the Web has increased dramatically. The potential of this vast amount of data is enormous but in most cases it is difficult for users to explore and use this data, especially for those without experience with Semantic Web technologies. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. In this article we devise a formal Linked Data Visualization Model (LDVM), which allows to dynamically connect data with visualizations. We report about our implementation of the LDVM comprising a library of generic visualizations that enable both users and data analysts to get an overview on, visualize and explore the Data Web and perform detailed analyzes on Linked Data.

Physicians are overwhelmed with many different drugs and the need to know a lot of information about all of them. That is, however, almost impossible in the fast evolving area of pharmaceutical industry. Although many data sources about drugs are published on the Web, structured or unstructured, it is very time consuming to search through them. In this paper we identify these data sources according to information needs of physicians. We show that they can be relatively easily integrated using the Linked Data principles and, in case of unstructured data, NLP methods. An application on the top of the integrated data sets is presented as a possible tool for clinical decision support.

XML has achieved the leading role among languages for data representation and, thus, the amount of related technologies and applications exploiting them grows fast. However, only a small percentage of applications is static and remains unchanged since its first deployment. Most of the applications change with newly coming user requirements and changing environment. In this paper we describe a framework and a methodology for management of evolution and change propagation throughout XML applications. We also introduce its proof-of-concept implementation called eXolutio, which has been developed and improved in our research group during last few years. We also provide an evaluation of the methodology in the domain of electronic health.

The most common applications of the today's IT world are information systems. The problems related to their design and implementation have sufficiently been solved. However, the true problems occur when an IS is already deployed and user requirements change. Currently this situation requires a skilled IT expert who knows all system components and, hence, is able to identify and modify all the affected parts. However, not always we have such an expert, whereas for complex systems it is a very hard and error-prone task. In this paper we introduce DaemonX -- an evolution management framework, which enables to manage evolution of complex applications efficiently and correctly. Using the idea of plug-ins, it enables to model almost any kind of a data format (currently XML, UML, ER, and BPMN). Since it preserves relationships among the modeled constructs, it naturally supports propagation of changes to all related affected parts. We describe the general proposal of the framework and, then, its architecture and implementation.

In this paper we introduce Strigil, a framework for automated data extraction. It represents an easily configurable tool that enables one to retrieve a data from textual or weak-structured documents. The paper contains description of the framework architecture and its important components. Additionally, we propose a scraping language inspired by the XSL transformations designed to extract data from different kinds of documents. Although there are many different approaches focused on various aspects of data scraping, they are usually very specialized to a concrete domain or a data source. We compare these solutions and discuss their advantages and disadvantages. Our scraping language is designed to work with an ontology to map scraped data directly to classes and attributes.

In this paper we briefly describe a new conceptual model for XML called XSEM. It is a combination of several approaches in the area. It divides the conceptual modeling process to conceptual and structural level. At the conceptual level, we design an overall conceptual schema of a domain independently on the required hierarchical representations of the data in XML documents. At the structural level, we design required hierarchical representations of the modeled data in different types of XML documents. In this paper, we further extend XSEM for modeling IS-A hierarchies. We also show how conceptual XSEM schemes can be represented at the logical level in XML schemes specified in the XML Schema language.