Towards an Open Research Knowledge Graph

Abstract

Knowledge graphs facilitate the discovery of information by organizing it into entities and describing the relationships of those entities to each other and to established ontologies. They are popular with search and e-commerce companies and could address the biggest problems in scientific communication, according to Sören Auer of the Technische Informationsbibliothek and Leibniz University of Hannover. In his NASIG vision session, Auer introduced attendees to knowledge graphs and explained how they could make scientific research more discoverable, efficient, and collaborative. Challenges include incentivizing researchers to participate and creating the training data needed to automate the generation of knowledge graphs in all fields of research.

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

“…Other interesting challenge is the study of different types of recommendation approaches, like collaborative filtering [63] or trust-based recommendation [64]. Finally, the development of crowdsourcing techniques in order to collaboratively create the knowledge base is also planned as future work (e.g., in the sense of [65] that proposes the application of crowdsourcing and some techniques from open innovation to the scientific method for creating a collective intelligence), since it includes many challenges, as stated by [17], such as encouraging researchers to create training data needed to automate the generation of knowledge bases in different fields of research.…”

“…According to [16], open science can be defined as "transparent and accessible knowledge that is shared and developed through collaborative networks". Open science thus implies research data sharing and stewardship to automate the generation of knowledge bases in different fields of research [17]. Interestingly, a recent study [18] argues that researchers are willing to share data due to a variety of factors, such as (i) regulative pressure by journals and normative pressure at a discipline level; and (ii) perceived career benefit and scholarly altruism at an individual level.…”

“…https://archive.ics.uci.edu/ml/datasets.html17 Authors of the paper play the role of expert data miners…”

Participatory design for ontologies: a case study of an open science ontology for qualitative coding schemas

“…Since open science, FAIR data principles (Mons et al, 2017) and the concept of research graphs (Auer, 2018) have become more important topics also in qualitative research; more effort has been invested in opening up qualitative research. FAIR data describe standards for research data.…”

A Memory Institution for the Digital Age

“…Furthermore, it could help optimise peer-review, reproducibility analysis, or comparison studies. The project Open Research Knowledge Graph (https:// projects.tib.eu/orkg/), funded by the European Research Council, attempts to create a semantic web of research literature to approach the vision of an intelligent web of human-and machine readable scholarly communication [2]. Further notable projects include the platform SlideWiki [3] to openly share and transparently develop educational content or big data best practices and infrastructure for Industry 4.0 as dealt with in the Horizon 2020 project BOOST 4.0 (https://boost40.eu/).…”