A Software Reference Architecture for Journalistic Knowledge Platforms

Abstract

Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledge-based systems to support efficient and high-quality journalism. However, making the different systems work together remains a challenge, calling for new unified journalistic knowledge platforms. A software reference architecture for journalistic knowledge platforms could help news organisations by capturing tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature. Therefore, this article proposes a software reference architecture for integrating artificial intelligence and knowledge bases to support journalists and newsrooms. The design of the proposed architecture is grounded on the research literature and on our experiences with developing a series of prototypes in collaboration with industry. Our aim is to make it easier for news organisations to evolve their existing independent systems for news production towards integrated knowledge platforms and to direct further research. Because journalists and newsrooms are early adopters of integrated knowledge platforms, our proposal can hopefully also inform architectures in other domains with similar needs.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.