Context

Document-based workflows in science have reached (or already exceeded) the limits of adequacy, as demonstrated, for example, by recent discussions about the proliferation of scientific literature and the reproducibility crisis. Although digital access to scientific publications has greatly improved in recent decades, even now that the papers are digitized, they remain document-based, making it difficult to communicate the knowledge they contain. This first step towards digitalization, however, makes it possible to rethink the dominant paradigm of document-centric knowledge sharing and transform it into knowledge-based information flows by expressing these papers through more flexible, fine-grained, semantic, interlinked, and context-sensitive representations in the form of knowledge graphs. The Open Research Knowledge Graph (ORKG) is one concrete research infrastructure that uses a knowledge graph as the underlying data structure to acquire, curate, publish, and process scholarly contributions from scientific papers in a structured and semantic form.

Interactive Session

First, we briefly present the idea of the ORKG and its basic functions to establish knowledge-based information flows by creating and evolving information models for a common understanding of data, information, and knowledge between the various scholars. By integrating these information models into existing and new research infrastructure services, scholarly contributions, that are currently implicit and deeply buried in documents, can be made explicit and directly usable. This new way of representing scholarly contributions has the potential to revolutionize scientific work by seamlessly linking research results and better mapping them to complex information needs. In this way, we transform scholarly contributions from papers into digital scientific knowledge that is easier to compare and reuse.

The main part of the session focuses on developing ideas in small groups about how we as CrowdRE researchers can use the ORKG to acquire, curate, publish, and process scholarly knowledge about CrowdRE research.

Here are some example questions that can be discussed in the small groups:

1. What research problems in CrowdRE do we need to address in the longer term?

2. What overviews of CrowdRE research do we need by default and must be maintained in the long term, as they affect all or a large group of CrowdRE researchers?

3. Who would be interested in setting up a so-called observatory (a group of experts whose task is to acquire and curate knowledge about CrowdRE)?