Crossing Borders, Crossing Cultures. Popular print in Europe

This volume explores the challenges and possibilities of research into the European dimensions of popular print culture. Popular print culture has traditionally been studied with a national focus. Recent research has revealed, however, that popular print culture has many European dimensions and shared features. A group of specialists in the field has started to explore the possibilities and challenges of research on a wide, European scale. This volume contains the first overview and analysis of the different approaches, methodologies and sources that will stimulate and facilitate future comparative research. This volume first addresses the benefits of a media-driven approach, focussing on processes of content recycling, interactions between text and image, processes of production and consumption. A second perspective illuminates the distribution and markets for popular print, discussing audiences, prices and collections. A third dimension refers to the transnational dimensions of genres, stories, and narratives. A last perspective unravels the communicative strategies and dynamics behind European bestsellers. This book is a source of inspiration for everyone who is interested in research into transnational cultural exchange and in the fascinating history of popular print culture in Europe

CONGAS: a collaborative ontology development framework based on Named GrAphS

The process of ontology development involves a range of skills and know-how often requiring team work of different people, each of them with his own way of contributing to the definition and formalization of the domain representation. For this reason, collaborative development is an important feature for ontology editing tools, and should take into account the different characteristics of team participants, provide them with a dedicated working environment allowing to express their ideas and creativity, still protecting integrity of the shared work. In this paper we present CONGAS, a collaborative version of the Knowledge Management and Acquisition platform Semantic Turkey which, exploiting the potentialities brought by recent introduction of context management into RDF triple graphs, offers a collaborative environment where proposals for ontology evolution can emerge and coexist, be evaluated by team users, trusted across different perspectives and eventually converged into the main development stream

Using Biographical Texts as Linked Data for Prosopographical Research and Applications

This paper argues that representing texts as semantic Linked Data provides a useful basis for analyzing their contents in Digital Humanities research and for Cultural Heritage application development. The idea is to transform Cultural Heritage texts into a knowledge graph and a Linked Data service that can be used flexibly in different applications via a SPARQL endpoint. The argument is discussed and evaluated in the context of biographical and prosopographical research and a case study where over 13 000 life stories form biographical collections of Biographical Centre of the Finnish Literature Society were transformed into RDF, enriched by data linking, and published in a SPARQL endpoint. Tools for biography and prosopography, data clustering, network analysis, and linguistic analysis were created with promising first results.Peer reviewe

Software Testing Techniques Revisited for OWL Ontologies

Ontologies are an essential component of semantic knowledge bases and applications, and nowadays they are used in a plethora of domains. Despite the maturity of ontology languages, support tools and engineering techniques, the testing and validation of ontologies is a field which still lacks consolidated approaches and tools. This paper attempts at partly bridging that gap, taking a first step towards the extension of some traditional software testing techniques to ontologies expressed in a widely-used format. Mutation testing and coverage testing, revisited in the light of the peculiar features of the ontology language and structure, can can assist in designing better test suites to validate them, and overall help in the engineering and refinement of ontologies and software based on them

On exploiting transformers for detecting explicit song lyrics

Determining if the lyrics of a given song could be hurtful or inappropriate for children is of utmost importance to prevent the reproduction of songs whose textual content is unsuitable for them. This problem can be computationally tackled as a binary classification task, and in the last couple of years various machine learning approaches have been applied to perform this task automatically. In this work, we investigate the automatic detection of explicit song lyrics by leveraging transformer-based language models, i.e., large language representations, unsupervisely built from huge textual corpora, that can be fine-tuned on various natural language processing tasks, such as text classification. We assess the performance of various transformer-based language model classifiers on a dataset consisting of more than 800K lyrics, marked with explicit information. The evaluation shows that while the classifiers built with these powerful tools achieve state-of-the-art performance, they do not outperform lighter and computationally less demanding approaches. We complement this empirical evaluation with further analyses, including an assessment of the performance of these classifiers in a few-shot learning scenario, where they are trained with just few thousands of samples