Knowledge reuse can increase the quality and efficiency of different activities, as well as reduce risks. The digital transformation of a city is a complex task that requires new approaches and management technologies. Knowledge reuse can reinforce city digital transformation initiatives, e.g. data integration and modeling activities can be improved via ontology reuse, design and development of digital solutions can be enhanced via reference architectures and reuse of existing methods and systems. Such reuse can speed up the work of city development managers, digital architects, and other ICT specialists, as well as decrease costs and risks. Although there are platforms for knowledge sharing and reuse within the smart city domain, they apply a traditional document-oriented approach to knowledge representation. This limits knowledge integration and the creation of intelligent services. So, knowledge graph technology was selected for collecting and curating digital city planning reusable content. The Open Research Knowledge Graph (ORKG) provided an opportunity for piloting this approach. The current paper will overview the digital city planning observatory within the ORKG, including main knowledge representation mechanisms and representative content examples, and provide the link between content and application scenarios.
Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari’s The Lives of The Artists . This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed.
Semantic Storytelling is the (semi)-automatic generation of new content (storylines) based on information extracted from document collections presented with helpful visualisation techniques. This paper summarises our previous work and describes the Semantic Storytelling vision and technical approach. We describe experiments that focus on the identification of relations between text segments extracted from documents written by different authors; discourse relations have, so far, primarily been researched within single documents only. The results confirm our intuition that discourse parsing is difficult to apply to the inter-text level, but they are encouraging as a first step. Similarly, the identification of inter-text relations using pairwise document classification yields promising results. Lastly, we show the effectiveness of paragraph ordering for coherent story generation.
This paper analyses the correlation between online news articles’ credibility and compliance with journalistic standards and ethics. Our main hypothesis is that news sources with low credibility adhere less to journalistic standards and ethics than high credibility sources. Additionally, the influence of web page layout factors is analysed. Our definition of metrics is based on the existing literature regarding journalistic best practices, as well as the list of credibility signals provided by the W3C Credible Web Community Group. News article credibility is assessed based on a score, which is calculated as a sum of linear subscores for the identified metrics divided in four credibility signal categories: Formality, Neutrality, Transparency and Layout. A curated dataset of 250 recent news items from known fake news sources, as well as 200 news items from established real news sources, is used in the testing phase. Although the comparison between real news and false news shows that, on average, the credibility score of genuine news is only 5% higher than that of fake news, the results do show more significant differences for certain subcategories and signals.
Digital Maktaba (DM) is an interdisciplinary project to create a digital library of texts in non-Latin alphabets (Arabic, Persian, Azerbaijani). The dataset is made available by the digital library heritage of the ”La Pira” library in the history and doctrines of Islam based in Palermo, which is the hub of the Foundation for Religious Sciences (FSCIRE, Bologna). Establishing protocols for the creation, maintenance and cataloguing of historical content in non-Latin alphabets is the long-term goal of DM. The first step of this project was to create an innovative workflow for automatic extraction of information and metadata from title pages of Arabic script texts. The Optical Character Recognition (OCR) tool uses various recognition systems, text processing techniques and corpora in order to provide accurate extraction and metadata of document content. In this paper we address the ongoing development of this novel tool and, for the first time, we present a demo of the current version that we have designed for the extraction and cataloguing process by showing a use case on an Arabic book frontispiece. In particular, we delve into the details of the tool workflow for automatically converting and uploading PDFs from the digital library, for the automatic extraction of cataloguing metadata and the semiautomatic (at the current stage) process of cataloguing. We also shortly discuss future prospects and the many additional features that we are planning to develop.
