The Cross-Domain Interoperability Framework (CDIF) is designed to support FAIR implementation by establishing a ‘lingua franca’, based on existing standards and technologies to support interoperability, in both human- and machine-actionable fashion. CDIF is a set of implementation recommendations, based on profiles of common, domain-neutral metadata standards which are aligned to work together to support core functions required by FAIR. This report presents a core set of five CDIF profiles, which address the most important functions for cross-domain FAIR implementation.  Discovery (discovery of data and metadata resources) Data access (specifically, machine-actionable descriptions of access conditions and permitted use) Controlled vocabularies (good practices for the publication of controlled vocabularies and semantic artefacts) Data integration (description of the structural and semantic aspects of data to make it integration-ready) Universals (the description of ‘universal’ elements, time, geography, and units of measurement). Each of these profiles is supported by specific recommendations, including the set of metadata fields in specific standards to use, and the method of implementation to be employed for machine-level interoperability. A further set of topics is examined, establishing the priorities for further work. These include: Provenance (the description of provenance and processing) Context (the description of ‘context’ in the form of dependencies between fields within the data and a description of the research setting) Perspectives on AI (discussing the impacts of AI and the role that metadata can play) Packaging (the creation of archival and dissemination packages)  Additional Data Formats (support for some of the data formats not fully supported in the initial release, such as NetCDF, Parquet, and HDF5).  In each of these topics, current discussions are documented, and considerations for further work are provided. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.

Establishing this open data portal to share our reference data and schema with customers, stakeholders, and partners under a permissive open data license.

Linked Data in Production: Moving Beyond Ontologies - Download as a PDF or view online for free

I keep checking in on the Solid project. But I’m baffled by its lack of functionality. I’ve written up some of my questions.

Abstract. We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata1 entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al., 2019), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG.

The scientific work of members of our Department was published in the monograph "Information Technology for Management: Approaches to Improving Business and Society" published by the Springer. The research concerns the automatic assessment of the quality of Wikipedia articles and the reliability of