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Large numbers of websites contain (human-readable) infor- mation about scheduled events, of which some may display media cap- tured at these events. This information is, however, often incomplete and always locked into the sites. This prevents users from creating overviews of media associated with an event from multiple websites. We carried out exploratory user studies with potential end-users to guide the design of a web-based environment for supporting event-based services. Based on our results, our goal is to provide support for exploring and selecting events and associated media, and for discovering meaningful, surprising or entertaining connections between events, media and participants by consuming linked data. We assembled a large collection of event and as- sociated media descriptions, which we interlinked with the Linked Open Data cloud. The dataset is obtained from three large public event directories (last.fm, eventful, upcoming) represented with the LODE ontology and from large media directories (ickr, youtube) represented with the Media Ontology.We present the results from the user studies, the conversion, interlinking and publication of the data following the best practices of the Semantic Web community, and our initial application design.

http://www.w3.org/2005/Incubator/prov/XGR-prov/

This paper describes STILTool, an open-source tool for the automatic evaluation of the quality of semantic annotations computed by semantic table interpretation approaches. STILTool provides a graphical interface allowing users to analyse the correctness of the annotations of tabular data. The tool also provides a set of statistics in order to identify the most common error patterns.

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web.

Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure., In: The Semantic Web - ISWC 2019. Lecture Notes in Computer Science, vol 11779. Springer, Cham

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Nowadays, a website is used to disseminate information about an event (e.g., location, dates, time). In the academic world, it is common to develop a website for an event, such as workshops or conferences. Aligning with the “Web of data”, its dissemination should also happen by publishing the information of the event as a knowledge graph, e.g., via RDF that is available through a SPARQL endpoint or a Triple Patterns Fragment server. However, the RDF generation and website development is not always straightforward and can be time-consuming. In this demo, we present the Semantic Academic-event Dissemination (SAD) Generator for generating RDF and websites for academic events. The generator allows to (i) annotate CSV files that contain academic event data and use the annotations to generate a knowledge graph and (ii) generate a website with the information for the event querying the knowledge graph. We used our generator to generate the RDF and website of a real workshop, the KGB workshop. It can be easily reused by organizers of other academic events by simply providing the event’s information in CSV files.

A new transparency model with more and better corporate data is necessary to promote sustainable economic growth. In particular, there is a need to link factors regarding non-nancial performance of corporations - such as social and environmental impacts, both positive and negative - into decision-making processes of investors and other stakeholders. To do this, we need to develop better ways to access and analyse corporate social, environmental and nancial performance information, and to link together insights from these dierent sources. Such sources are already on the web in non-structured and structured data formats, a big part of them in XBRL (Extensible Business Reporting Language). This study is about promoting solutions to drive eective transparency for a sustainable economy, given the current adoption of XBRL, and the new opportunities that Linked Data can oer. We present (1) a methodology to formalise XBRL as RDF using Linked data principles and (2) demonstrate its usefulness through a use case connecting and making the data accessible.

Smart Contracts have emerged as a novel way to automate the execution of contracts in a decentralised and secure environment, minimising the risk of breach or non-compliance. However, recent research points out that the same measures that secure Smart Contracts against disruption with the purpose of breach makes altering the terms, rights and conditions of contracts difficult. The same research proposes a set of standards inspired in paper-contract law that Smart Contract platforms should implement to enable Smart Contract Undo and Alteration. This paper is about preliminary work on describing terms, rights, and conditions of Smart Contracts as RDF documents linked to them, levering Semantic Web tools enabling: (i) Definition and checking of complex rights and conditions (ii) Separation of the terms of the contract from its execution logic. (iii) Querying and Updating via SPARQL (iv) Alteration of terms that were not initially considered as modifiable.

Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL \({\mathcal {ALC}}\). We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.

Inspired by insights from theories of human reasoning and language, we propose additions to the Manchester OWL Syntax to improve comprehensibility. These additions cover: functional and inverse functional properties, negated conjunction, the definition of exceptions, and existential and universal restrictions. By means of an empirical study, we demonstrate the effectiveness of a number of these additions, in particular: the use of solely to clarify the uniqueness of the object in a functional property; the replacement of and with intersection in conjunction, which was particularly beneficial in negated conjunction; the use of except as a substitute for and not; and the replacement of some with including and only with noneOrOnly, which helped in certain situations to clarify the nature of these restrictions.