The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.
%0 Journal Article
%1 auer2020improving
%A Auer, Sören
%A Oelen, Allard
%A Haris, Muhammad
%A Stocker, Markus
%A D’Souza, Jennifer
%A Farfar, Kheir Eddine
%A Vogt, Lars
%A Prinz, Manuel
%A Wiens, Vitalis
%A Jaradeh, Mohamad Yaser
%D 2020
%J Bibliothek Forschung und Praxis
%K myown
%N 3
%R 10.1515/bfp-2020-2042
%T Improving Access to Scientific Literature with Knowledge Graphs
%U https://doi.org/10.1515/bfp-2020-2042
%V 44
%X The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.
@article{auer2020improving,
abstract = {The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.},
added-at = {2022-02-23T10:31:39.000+0100},
author = {Auer, Sören and Oelen, Allard and Haris, Muhammad and Stocker, Markus and D’Souza, Jennifer and Farfar, Kheir Eddine and Vogt, Lars and Prinz, Manuel and Wiens, Vitalis and Jaradeh, Mohamad Yaser},
biburl = {https://www.bibsonomy.org/bibtex/2629632c87995bf35b9a2a718cc91dc8d/soeren},
doi = {10.1515/bfp-2020-2042},
interhash = {7a052de1c307ea42801bbb8cbd98aed3},
intrahash = {629632c87995bf35b9a2a718cc91dc8d},
issn = {1865-7648},
journal = {Bibliothek Forschung und Praxis},
keywords = {myown},
number = 3,
timestamp = {2022-02-23T10:31:39.000+0100},
title = {Improving Access to Scientific Literature with Knowledge Graphs},
url = {https://doi.org/10.1515/bfp-2020-2042},
volume = 44,
year = 2020
}