Response to Wyssusek’s “On Ontological Foundations of Conceptual Modelling”

Abstract not available

A Roadmap for UEML

International audienceA Roadmap for Unified enterprise modelling languag

Analysis and Design of Computational News Angles

A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space

Process change projects: a study of Norwegian practice

Process change, in various incarnations, has been a central topic in the IS field for several decades. This paper presents an overview of Norwegian model-supported process-change practice, based on in-depth interviews of 33 informants, each describing a different process-change effort in one of 30 Norwegian enterprises. The overview focusses on use of process models, present versus future focus, ICT as enabler of change, participation, resistance to change and process ownership. Norwegian practice is then compared with the predominantly North-American process-change literature from a national-culture perspective. In particular, we find that stakeholder participation is high in Norwegian process-change projects and that resistance tends to be low, a finding consistent with theory on national-culture differences. The paper presents the first results from a larger project that aims to contribute towards a theory of model-based process change

The News Angler Project: Exploring the Next Generation of Journalistic Knowledge Platforms

The News Angler project aims to support journalists in finding new and unexpected connections and angles in the news. The project therefore explores how recent artificial intelligence (AI) techniques — such as knowledge graphs, natural-language processing (NLP) and machine learning (ML) — can support high-quality journalism that exploits big and open data sources. A central contribution is News Hunter, a series of prototype journalistic knowledge platforms (JKPs)

Trustworthy journalism through AI

Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust

Responsible media technology and AI: challenges and research directions

The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed