Applying VocPrez to operational semantic repositories: the NVS experience

<p>The Natural Environment Research Council’s (NERC) Vocabulary Server (NVS<sup>1</sup>) has been serving the marine and wider community with controlled vocabularies for over a decade. NVS provides access to standardised lists of terms which are used for data mark-up, facilitating interoperability and discovery in the marine and associated earth science domains. The NVS controlled vocabularies are published as Linked Data on the web using the data model of the Simple Knowledge Organisation System (SKOS). They can also be accessed as web services (RESTFul, SOAP) or through a sparql endpoint. NVS is an operational semantic repository, which underpins data systems like SeaDataNet, the pan-European infrastructure of marine data management, and is embedded in SeaDataNet-specific tools like MIKADO. Its services are being constantly monitored by the SeaDataNet Argo monitoring system, ensuring a guarantee of reliability and availability. In this presentation we will discuss the pathway of challenges we encountered while enhancing an operational semantic repository like NVS with VocPrez, a read-only web delivery system for Simple Knowledge Organization System (SKOS)-formulated RDF vocabularies. We will also present our approach on implementing CI/CD delivery and the added value of VocPrez to NVS in terms of FAIRness. Finally we will discuss the lessons learnt during the lifecycle of this development. </p><p>VocPrez<sup>2</sup> is an open-source, pure Python, application that reads vocabularies from one or more sources and presents them online (HTTP) in several different ways: as human-readable web pages, using simple HTML templates for different SKOS objects and as machine-readable RDF or other formats, using mapping code. The different information model views supported by VocPrez are defined by profiles, that is, by formal specifications. VocPrez supports both different profiles and different formats (Media Types) for each profile.</p><p>VocPrez enhanced the publication of NVS both for human users and machines. Humans accessing NVS are presented with a new look and feel that is more user friendly, providing filtering of collections, concepts and thesauri, and sorting of results using different options. For machine-to-machine communication, VocPrez presents NVS content in machine-readable formats which Internet clients can request directly using the Content Negotiation by Profile standard<sup>3</sup>. The profiles and formats available are also listed on an “Alternate Profiles” web page which is automatically generated per resource thus allowing for discovery of options. As a result, human or machine end users can access NVS collections, thesauri and concepts according to different information models such as DCAT, NVS’ own vocabulary model or pure SKOS and also in different serializations like JSON-LD , turtle, etc. using content negotiation. </p><p><sup>1</sup>http://vocab.nerc.ac.uk/</p><p><sup>2</sup>https://github.com/RDFLib/VocPrez</p><p><sup>3</sup>https://www.w3.org/TR/dx-prof-conneg/</p>

Quantifying the Connectivity of a Semantic Warehouse and Understanding Its Evolution Over Time

In many applications one has to fetch and assemble pieces of information coming from more than one source for building a semantic warehouse offering more advanced query capabilities. In this paper the authors describe the corresponding requirements and challenges, and they focus on the aspects of quality and value of the warehouse. For this reason they introduce various metrics (or measures) for quantifying its connectivity, and consequently its ability to answer complex queries. The authors demonstrate the behaviour of these metrics in the context of a real and operational semantic warehouse, as well as on synthetically produced warehouses. The proposed metrics allow someone to get an overview of the contribution (to the warehouse) of each source and to quantify the value of the entire warehouse. Consequently, these metrics can be used for advancing data/endpoint profiling and for this reason the authors use an extension of VoID (for making them publishable). Such descriptions can be exploited for dataset/endpoint selection in the context of federated search. In addition, the authors show how the metrics can be used for monitoring a semantic warehouse after each reconstruction reducing thereby the cost of quality checking, as well as for understanding its evolution over time.

Connectivity, Value, and Evolution of a Semantic Warehouse

In many applications, one has to fetch and assemble pieces of information coming from more than one source for building a semantic warehouse offering more advanced query capabilities. This chapter describes the corresponding requirements and challenges, and focuses on the aspects of quality, value and evolution of the warehouse. It details various metrics (or measures) for quantifying the connectivity of a warehouse and consequently the warehouse's ability to answer complex queries. The proposed metrics allow someone to get an overview of the contribution (to the warehouse) of each source and to quantify the value of the entire warehouse. Moreover, the paper shows how the metrics can be used for monitoring a warehouse after a reconstruction, thereby reducing the cost of quality checking and understanding its evolution over time. The behaviour of these metrics is demonstrated in the context of a real and operational semantic warehouse for the marine domain. Finally, the chapter discusses novel ways to exploit such metrics in global scale and for visualization purposes.

Embodied cognitive semantics for quantification

The present paper proposes an operational semantic model of natural language quantifiers (e.g., many, some, three) and their use in quantified noun phrases. To this end we use embodied artificial agents that communicate in and interact with the physical world. We argue that existing paradigms such as Generalized Quantifiers (Barwise and Cooper 1981; Montague 1973) and Fuzzy Quantifiers (Zadeh 1983) do not provide a satisfactory models for our situated-interaction scenarios and propose a more adequate semantic model, based on fuzzy-quantification.

Quantifying the Connectivity of a Semantic Warehouse and Understanding its Evolution over Time

In many applications one has to fetch and assemble pieces of information coming from more than one source for building a semantic warehouse offering more advanced query capabilities. In this paper the authors describe the corresponding requirements and challenges, and they focus on the aspects of quality and value of the warehouse. For this reason they introduce various metrics (or measures) for quantifying its connectivity, and consequently its ability to answer complex queries. The authors demonstrate the behaviour of these metrics in the context of a real and operational semantic warehouse, as well as on synthetically produced warehouses. The proposed metrics allow someone to get an overview of the contribution (to the warehouse) of each source and to quantify the value of the entire warehouse. Consequently, these metrics can be used for advancing data/endpoint profiling and for this reason the authors use an extension of VoID (for making them publishable). Such descriptions can be exploited for dataset/endpoint selection in the context of federated search. In addition, the authors show how the metrics can be used for monitoring a semantic warehouse after each reconstruction reducing thereby the cost of quality checking, as well as for understanding its evolution over time.