Analysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies

Abstract

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications, and outline possible solutions.

A Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation

“…For the development of the ORKG dashboard, we considered the list of tasks that a scholarly knowledge graph should enable a researcher to complete: get a research field overview; find related work; assess relevance; extract relevant information; get recommended articles; obtain deep understanding; and reproduce results (Brack et al, 2021 ).…”

Cross-domain multi-task learning for sequential sentence classification in research papers

“…Furthermore, we briefly review transfer learning methods. For a more comprehensive overview about information extraction from scientific text, we refer to Brack et al [9] and Nasar et al [49].…”

“…Scientists usually use academic search engines and skim through the text of the found articles to assess their relevance. However, academic search engines cannot assist researchers adequately in these tasks since most research papers are plain PDF files and not machine-interpretable [9,60,74]. The exploding number of published articles aggravates this situation further [7].…”

CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science

“…However, the current document-centric scholarly communication paradigm does not enable scholars to efficiently explore, categorize, and reason on this knowledge [17]. Scientists need instead to find and manually analyze large number of static PDF files in order to gain a (often incomplete) understanding about recent research advancements [9].…”