As part of modern research practices, the FAIR data principles have become essential for data discoverability, usability, and sharing.Existing implementations for automatically assessing FAIR adherence (FAIRness) often suffer from limited usability, inconsistent accuracy, and difficult-to-interpret results, as they require explicit rules to cover for specific FAIR assessment frameworks, which are not easy to generalize.This paper introduces Fair-Way, an open source tool that leverages Large Language Models (LLMs) to automate FAIRness assessment.Fair-Way applies a divide-and-conquer approach to decompose the assessment process into fine-grained tasks, as well as to split the metadata into manageable chunks.Evaluation demonstrates that Fair-Way achieves performance comparable to existing tools, while outperforming them in several key metrics.Moreover, Fair-Way generalizes across FAIR assessment indicators without requiring explicitly programmed logic and supports both structured and unstructured metadata in diverse formats.Finally, it enables user-defined, domain-specific tests, which are typically not supported by other systems.Overall, Fair-Way represents a scalable and flexible solution to accelerate FAIR data practices across research domains.

Integrated and efficient mobility requires data sharing among the involved stakeholders. In this direction, regulators and transport authorities have been defining policies to foster mobility data’s digitalisation and online publication. However, creating several heterogeneous data portals for mobility data resulted in a fragmented ecosystem that challenges data accessibility. In this context, metadata is a key enabler to foster the findability and reusability of relevant datasets, but their interoperability across different data portals should be ensured. Moreover, each domain presents specificities on the relevant information that should be encoded through metadata. To solve these issues within the mobility domain, we present mobilityDCAT-AP, a reference metadata specification for mobility data portals defined by putting together domain experts and the Semantic Web community. We report on the work done to develop the metadata model behind mobilityDCAT-AP and the best practices followed in its implementation and publication. Finally, we describe the available educational resources and the activities performed to ensure broader adoption of mobilityDCAT-AP across mobility data portals. We present success stories from early adopters and discuss the challenges they encountered in implementing a metadata specification based on Semantic Web technologies. Resource type: Metadata Specification License: Creative Commons Attribution 4.0 License PID: https://w3id.org/mobilitydcat-ap Repository: https://github.com/mobilityDCAT-AP

Information interoperability is of paramount importance to Industry 4.0 (I4.0). To this end, the Reference Architecture Model for I4.0 (RAMI4.0) defines the Asset Administration Shell (AS) concept as a core element for interoperable descriptions of assets. Assets, such as Cyber-Physical Systems, are building blocks that need to be interchangeable between various industrial systems but are heterogeneously modeled. In order to achieve the required interoperability between these systems, semantics is key. Typically, two types of systems occur in I4.0 scenarios: legacy systems not considering explicit semantics, and a new generation of ontologybased systems. However, existing approaches for modeling the AS do not explicitly address this situation of interoperability between systems of such different types. In this article, we develop an ontological view of the AS concept to bridge the gap between these types of systems. This results in an ontology that is intended to be lik ewise realizable with both ontology-based and non-ontology-based systems. We present this ontology together with a suggested two-phase engineering process for its application.

Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.

Abstract In this article we describe the Linked Data Notifications (LDN) protocol, which is a W3C Candidate Recommendation. Notifications are sent over the Web for a variety of purposes, for example, by social applications. The information contained within a notification is structured arbitrarily, and typically only usable by the application which generated it in the first place. In the spirit of Linked Data, we propose that notifications should be reusable by multiple authorised applications. Through separating the concepts of senders, receivers and consumers of notifications, and leveraging Linked Data principles of shared vocabularies and URIs, LDN provides a building block for decentralised Web applications. This permits end users more freedom to switch between the online tools they use, as well as generating greater value when notifications from different sources can be used in combination. We situate LDN alongside related initiatives, and discuss additional considerations such as security and abuse prevention measures. We evaluate the protocol’s effectiveness by analysing multiple, independent implementations, which pass a suite of formal tests and can be demonstrated interoperating with each other. To experience the described features please open this document in your Web browser under its canonical URI: http://csarven.ca/linked-data-notifications .

Abstract While the Web was designed as a decentralised environment, individual authors still lack the ability to conveniently author and publish documents, and to engage in social interactions with documents of others in a truly decentralised fashion. We present dokieli , a fully decentralised, browser-based authoring and annotation platform with built-in support for social interactions, through which people retain ownership of and sovereignty over their data. The resulting “living” documents are interoperable and independent of dokieli since they follow standards and best practices, such as HTML+RDFa for a fine-grained semantic structure, Linked Data Platform for personal data storage, and Linked Data Notifications for updates. This article describes dokieli’s architecture and implementation, demonstrating advanced document authoring and interaction without a single point of control. Such an environment provides the right technological conditions for independent publication of scientific articles, news, and other works that benefit from diverse voices and open interactions. To experience the described features please open this document in your Web browser under its canonical URI: http://csarven.ca/dokieli-rww .

Following the Linked Data principles means maximising the reusability of data over the Web. Reuse of datasets can become apparent when datasets are linked to from other datasets, and referred in scientific articles or community discussions. It can thus be measured, similarly to citations of papers. In this paper we propose dataset reuse metrics and use these metrics to analyse indications of dataset reuse in different communication channels within a scientific community. In particular we consider mailing lists and publications in the Semantic Web community and their correlation with data interlinking. Our results demonstrate that indications of dataset reuse across different communication channels and reuse in terms of data interlinking are positively correlated.

Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.

With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact, deterministic computation of the quality metrics is too time consuming or expensive. We employ probabilistic techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient estimation for implementing a broad set of data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu. We evaluated its performance and accuracy on Linked Open Datasets of broad relevance.