Jakub Klímek

To achieve interoperability for effective data exchange on the web, we need a contract. Depending on the field, we may refer to technical data schemas, web vocabularies, or, more generally, to data specifications (DSes). The development and management of these DSes can become difficult in complex domains with multiple stakeholders and related DSes involved. In this article, we present Dataspecer, an open-source, modular web application for the development of semantic data specifications (SDSes), DSes that target the semantic and technical layers of data exchange. Dataspecer allows users to design web vocabularies and their application profiles, maintaining relations between reused concepts and their original SDSes. Furthermore, Dataspecer assists users in the creation of technical artifacts such as schemas for JSON or XML, while maintaining consistency of the artifacts with the application profiles. We motivate the need for SDSes and derive requirements for such a tool. In case studies based on the ecosystem of Data Catalog Vocabulary-based specifications, we demonstrate that SDSes created in Dataspecer meet these requirements and are of higher quality. We show SDSes that were created directly in Dataspecer, and in the evaluation section, we argue that using our tool is more efficient than creating them manually, even for smaller domains.

The European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data as part of the Open Data Directive. One of the HVD topics is company data. Although the HVD description contains items that must be included in these datasets, it does not prescribe any technical means of how the data should be published. This is a major obstacle to the interoperability of the datasets once they are published. In this extended paper, we elaborate on the results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on various aspects of data interoperability of open data from business registries, covering the company data HVDs topic. These aspects include the semantic, technical, and legal interoperability of this data. The results include a data architecture and a data specification to make the published data technically and semantically interoperable. In addition, we present basic legal interoperability guidelines to ensure legal interoperability of the published data, which is a topic often neglected by technically focused data experts. The project results include proof-of-concept transformations of data from selected European business registries using open source tools and in accordance with the data specification. Moreover, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. Finally, we present an example of how compliant data can be processed by data experts for further analysis.

Purpose: Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.Design/methodology/approach: In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.Findings: The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.Originality/value: To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

There is a large number of datasets published as Linked (Open) Data (LOD/LD). At the same time, there is also a multitude of tools for publication of LD. However, potential LD consumers still have difficulty discovering, accessing and exploiting LD.