Ontologies for finding journalistic angles

Opdahl, Andreas L.; Tessem, Bjørnar

doi:10.1007/s10270-020-00801-w

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…The other knowledge sources only use raw text. A final knowledge source uses the outputs of the previous knowledge sources to represent their annotations as graphs following an event/item annotation ontology [18]. To conduct the experiment, we used feeds of news articles written in English.…”

Section: Discussionmentioning

confidence: 99%

“…In our use case, a stream of news (raw text) is semantically annotated with the concepts and relations in the text [3], [18], Figure 3 illustrates the instantiation of the annotation blackboard. In it, the different annotation tasks such as namedentity recognition (NER), relation extraction (RE), namedentity linking (NEL), co-reference, part-of-speech tagging (PoS) and others are types of knowledge sources.…”

Section: Nlp Annotation As a Blackboard Approachmentioning

confidence: 99%

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A Blackboard Model for Flexible and Parallel Text Annotation

Gallofré Ocaña,

Opdahl

2024

IEEE Access

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Creating rich semantic text annotations is a complex process that involves combining multiple natural-language annotation approaches. This annotation process is often approached sequentially and includes pre-processing steps and techniques that build on the outputs of others. However, combining them is not trivial, because some annotation approaches comprise chains of steps or build on other already preexisting annotations, some pre-processing steps may be common to several techniques, and many newer techniques are even end-to-end which have diluted the need for specific pre-processing steps. Yet it can be beneficial to combine the different approaches because they solve different annotation problems and, even when they solve the same problem, they may have complementary strengths. Whereas existing works often approach the annotation process sequentially, we argue that it can instead be implemented as a partly sequential, partly parallel and concurrent collaboration between independent components. The Blackboard Model is a long-established problem-solving paradigm that deals with complex problems where multiple knowledge sources contribute independently towards the solution. In this work, we study the feasibility of the Blackboard Model for creating rich semantic annotations from text as part of a larger big-data-ready AI system for supporting journalists and newsrooms.

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Section: Discussionmentioning

confidence: 99%

Section: Nlp Annotation As a Blackboard Approachmentioning

confidence: 99%

A Blackboard Model for Flexible and Parallel Text Annotation

Gallofré Ocaña,

Opdahl

2024

IEEE Access

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“…These JKPs continuously monitored and curated the annotated items using AI/ML and LOD to provide relevant insights for journalists and identify current, past and future events. For example, the annotated news items were used to identify networks of actors [15], suggest news angles [13,54], automate news creation [22] and facilitate factchecking [17], and the events were analysed using different AI/ML techniques for grouping events and news items [21,23], reasoning over events, and reconstructing the evolution of the events along time [15].…”

Section: Industry Partners Countries Referencesmentioning

confidence: 99%

Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions

Ocaña

Opdahl

2022

Technologies

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Increasing competition and loss of revenues force newsrooms to explore new digital solutions. The new solutions employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. The result is an emerging type of intelligent information system we have called the Journalistic Knowledge Platform (JKP). In this paper, we analyse for the first time knowledge graph-based JKPs in research and practice. We focus on their current state, challenges, opportunities and future directions. Our analysis is based on 14 platforms reported in research carried out in collaboration with news organisations and industry partners and our experiences with developing knowledge graph-based JKPs along with an industry partner. We found that: (a) the most central contribution of JKPs so far is to automate metadata annotation and monitoring tasks; (b) they also increasingly contribute to improving background information and content analysis, speeding-up newsroom workflows and providing newsworthy insights; (c) future JKPs need better mechanisms to extract information from textual and multimedia news items; (d) JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers’ demands.

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“…A concern is to detect and defuse biases in what the algorithms consider newsworthy. Related techniques for representing news angles used by journalists to identify and frame newsworthy content are also under development [53,56]. The goal of this work is to provide computational support to generate interesting new stories that match the news values and angles of interest to a particular media organization.…”

Section: Media Content Analysis and Productionmentioning

confidence: 99%

Responsible media technology and AI: challenges and research directions

et al. 2021

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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.

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Ontologies for finding journalistic angles

Cited by 13 publications

References 38 publications

A Blackboard Model for Flexible and Parallel Text Annotation

A Blackboard Model for Flexible and Parallel Text Annotation

Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions

Responsible media technology and AI: challenges and research directions

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