Data privacy and sovereignty are open challenges in today’s Web, which the Solid ( https://solidproject.org ) ecosystem aims to meet by providing personal online datastores (pods) where individuals can control access to their data. Solid allows developers to deploy applications with access to data stored in pods, subject to users’ permission. For the decentralised Web to succeed, the problem of search over pods with varying access permissions must be solved. The ESPRESSO framework takes the first step in exploring such a search architecture, enabling large-scale keyword search across Solid pods with varying access rights. This paper provides a comprehensive experimental evaluation of the performance and scalability of decentralised keyword search across pods on the current ESPRESSO prototype. The experiments specifically investigate how controllable experimental parameters influence search performance across a range of decentralised settings. This includes examining the impact of different text dataset sizes (0.5 MB to 50 MB per pod, divided into 1 to 10,000 files), different access control levels (10%, 25%, 50%, or 100% file access), and a range of configurations for Solid servers and pods (from 1 to 100 pods across 1 to 50 servers). The experimental results confirm the feasibility of deploying a decentralised search system to conduct keyword search at scale in a decentralised environment.

The process of extracting, structuring, and organizing knowledge requires processing large and originally heterogeneous data sources. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on how to semantically annotate data, and generate and publish Linked Data. We introduce [R2]RML languages to generate Linked Data. We also show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish Linked Data on their own.

While humans browse the Web by following links, these hypermedia links can also be used by machines for browsing. While efforts such as Hydra semantically describe the hypermedia controls on Web interfaces to enable smarter interface-agnostic clients, they are largely limited to the input parameters to interfaces, and clients therefore do not know what response to expect from these interfaces. In order to convey such expectations, interfaces need to declaratively describe the response structure of their parameterized hypermedia controls. We therefore explored techniques to represent this parameterized response structure in a generic but expressive way. In this work, we discuss four different approaches for declaring a response structure, and we compare them based on a model that we introduce. Based on this model, we conclude that a SHACL shape-based approach can be used for declaring such a parameterized response structure, as it conforms to the REST architectural style that has helped shape the Web into its current form.