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

Abstract

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.

Design and Development of Knowledge Graph for Industrial Chain Based on Deep Learning

“…NER can be mainly categorized into three types [28]. The first type is rule-based or knowledge-based methods [30], which have the advantages of strong interpretability and good adaptability in specific domains. Due to their reliance on manually designed rules and domain expert knowledge, these methods do not require a large amount of annotated data.…”