Auer, S.; Oelen, A.; Haris, A.M.; Stocker, M.; D'Souza, J.; Farfar, K.E.; Vogt, L.; Prinz, M.; Wiens, V.; Jaradeh, M.Y.: Improving access to scientific literature with knowledge graphs : an experiment using library guidelines to judge information integrity (2020)
0.00
0.0049610245 = product of:
0.03472717 = sum of:
0.027592704 = weight(_text_:bibliothek in 316) [ClassicSimilarity], result of:
0.027592704 = score(doc=316,freq=2.0), product of:
0.121660605 = queryWeight, product of:
4.1055303 = idf(docFreq=1980, maxDocs=44218)
0.029633347 = queryNorm
0.22680065 = fieldWeight in 316, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1055303 = idf(docFreq=1980, maxDocs=44218)
0.0390625 = fieldNorm(doc=316)
0.0071344664 = weight(_text_:information in 316) [ClassicSimilarity], result of:
0.0071344664 = score(doc=316,freq=4.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.13714671 = fieldWeight in 316, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=316)
0.14285715 = coord(2/14)
- 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.
- Source
- Bibliothek: Forschung und Praxis. 44(2020) H.3, S.516-529
