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With the continuous growth of the Linked Data Cloud, adequate methods to efficiently explore semantic data are increasingly required. Faceted browsing is an established technique for exploratory search. Users are given an overview of a collection's attributes that can be used to progressively refine their filter criteria and delve into the data. However, manual facet predefinition is often inappropriate for at least three reasons: Firstly, heterogeneous and large scale knowledge graphs offer a huge number of possible facets. Choosing among them may be virtually impossible without algorithmic support. Secondly, knowledge graphs are often constantly changing, hence, predefinitions need to be redone or adapted. Finally, facets are generally applied to only a subset of resources (e.g., search query results). Thus, they have to match this subset and not the knowledge graph as a whole. Precomputing facets for each possible subset is impractical except for very small graphs. We present our approach for automatic facet generation and selection over knowledge graphs. We propose methods for (1) candidate facet generation and (2) facet ranking, based on metrics that both judge a facet in isolation as well as in relation to others. We integrate those methods in an overall system workflow that also explores indirect facets, before we present the results of an initial evaluation.

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<p>Global environmental challenges like climate change, pollution, and biodiversity loss are complex. To understand environmental patterns and processes and address these challenges, scientists require the observations of natural phenomena at various temporal and spatial scales and across many domains. The research infrastructures and scientific communities involved in these activities are often following their own data management practices which inevitably leads to a high degree of variability and incompatibility of approaches. Consequently, a variety of metadata standards and vocabularies have been proposed to describe observations and are actively used in different communities. However, this diversity in approaches now causes severe issues regarding the interoperability across datasets and hampers their exploitation as a common data source.</p><p>Projects like ENVRI-FAIR, FAIRsFAIR, FAIRplus are addressing this difficulty by working on the full integration of services across research infrastructures based on FAIR Guiding Principles supporting the EOSC vision towards an open research culture. Beyond these projects, we need collaboration and community consensus across domains to build a common framework for representing observable properties. The Research Data Alliance InteroperAble Descriptions of Observable Property Terminology Working Group (RDA I-ADOPT WG) was formed in October 2019 to address this need. Its membership covers an international representation of terminology users and terminology providers, including terminology developers, scientists, and data centre managers. The group’s overall objective is to deliver a common interoperability framework for observable property variables within its 18-month work plan. Starting with the collection of user stories from research scientists, terminology managers, and data managers or aggregators, we drafted a set of technical and content-related requirements. A survey of terminology resources and annotation practices provided us with information about almost one hundred terminologies, a subset of which was then analysed to identify existing conceptualisation practices, commonalities, gaps, and overlaps. This was then used to derive a conceptual framework to support their alignment. </p><p>In this presentation, we will introduce the I-ADOPT Interoperability Framework highlighting its semantic components. These represent the building blocks for specific ontology design patterns addressing different use cases and varying degrees of complexity in describing observed properties. We will demonstrate the proposed design patterns using a number of essential climate and essential biodiversity variables. We will also show examples of how the I-ADOPT framework will support interoperability between existing representations. This work will provide the semantic foundation for the development of more user-friendly data annotation tools capable of suggesting appropriate FAIR terminologies for observable properties.</p>

Units of measurement are an essential part of dataset descriptions as they are required for a valid interpretation of the data. One obvious choice for representing units are ontologies, but as every application supports different use cases a multitude of ontologies has been created. Each of these is suited best for just a subset of the possible use cases. The problem of choosing an ontology for a new project hence consists of two major aspects: What use cases need to be covered and which ontology caters best to them?We describe possible use cases and analyze their requirements. The results are then used to assess the modeling of the domain in different ontologies with respect to their suitability for those use cases. This analysis shows the differences in the support for different use cases. It can help developers to choose the best ontology for their specific needs and also highlights areas for further ontology improvement.