Improving Access to Scientific Literature with Knowledge Graphs

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...
Ausführliche Beschreibung

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Autor*in:	Auer, Sören [verfasserIn] Oelen, Allard Haris, Muhammad Stocker, Markus D’Souza, Jennifer Farfar, Kheir Eddine Vogt, Lars Prinz, Manuel Wiens, Vitalis Jaradeh, Mohamad Yaser

Format:	Artikel

Erschienen:	2020

Anmerkung:	© 2020 Walter de Gruyter GmbH, Berlin/Boston

Übergeordnetes Werk:	Enthalten in: Bibliothek - De Gruyter, 1977, 44(2020), 3 vom: 30. Nov., Seite 516-529
Übergeordnetes Werk:	volume:44 ; year:2020 ; number:3 ; day:30 ; month:11 ; pages:516-529

Links:	Volltext

DOI / URN:	10.1515/bfp-2020-2042

Katalog-ID:	OLC2142166024

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spelling	10.1515/bfp-2020-2042 doi (DE-627)OLC2142166024 (DE-B1597)bfp-2020-2042-p DE-627 ger DE-627 rakwb 000 010 030 050 060 070 080 370 700 VZ 020 VZ 24,1 ssgn BBI DE-23 fid Auer, Sören verfasserin aut Improving Access to Scientific Literature with Knowledge Graphs 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © 2020 Walter de Gruyter GmbH, Berlin/Boston 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. Oelen, Allard aut Haris, Muhammad aut Stocker, Markus aut D’Souza, Jennifer aut Farfar, Kheir Eddine aut Vogt, Lars aut Prinz, Manuel aut Wiens, Vitalis aut Jaradeh, Mohamad Yaser aut Enthalten in Bibliothek De Gruyter, 1977 44(2020), 3 vom: 30. Nov., Seite 516-529 (DE-627)12961193X (DE-600)243153-1 (DE-576)015107671 0341-4183 nnns volume:44 year:2020 number:3 day:30 month:11 pages:516-529 https://doi.org/10.1515/bfp-2020-2042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BBI SSG-OLC-BUB SSG-OLC-SPO SSG-OPC-BBI GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_50 GBV_ILN_61 GBV_ILN_70 GBV_ILN_74 GBV_ILN_90 GBV_ILN_104 GBV_ILN_121 GBV_ILN_123 GBV_ILN_130 GBV_ILN_150 GBV_ILN_151 GBV_ILN_179 GBV_ILN_184 GBV_ILN_216 GBV_ILN_267 GBV_ILN_293 GBV_ILN_754 GBV_ILN_2021 GBV_ILN_2024 GBV_ILN_2031 GBV_ILN_2046 GBV_ILN_2057 GBV_ILN_2060 GBV_ILN_2065 GBV_ILN_2170 GBV_ILN_2412 GBV_ILN_2415 GBV_ILN_2505 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4125 GBV_ILN_4247 GBV_ILN_4261 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4311 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4321 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 44 2020 3 30 11 516-529
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allfieldsGer	10.1515/bfp-2020-2042 doi (DE-627)OLC2142166024 (DE-B1597)bfp-2020-2042-p DE-627 ger DE-627 rakwb 000 010 030 050 060 070 080 370 700 VZ 020 VZ 24,1 ssgn BBI DE-23 fid Auer, Sören verfasserin aut Improving Access to Scientific Literature with Knowledge Graphs 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © 2020 Walter de Gruyter GmbH, Berlin/Boston 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. Oelen, Allard aut Haris, Muhammad aut Stocker, Markus aut D’Souza, Jennifer aut Farfar, Kheir Eddine aut Vogt, Lars aut Prinz, Manuel aut Wiens, Vitalis aut Jaradeh, Mohamad Yaser aut Enthalten in Bibliothek De Gruyter, 1977 44(2020), 3 vom: 30. Nov., Seite 516-529 (DE-627)12961193X (DE-600)243153-1 (DE-576)015107671 0341-4183 nnns volume:44 year:2020 number:3 day:30 month:11 pages:516-529 https://doi.org/10.1515/bfp-2020-2042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BBI SSG-OLC-BUB SSG-OLC-SPO SSG-OPC-BBI GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_50 GBV_ILN_61 GBV_ILN_70 GBV_ILN_74 GBV_ILN_90 GBV_ILN_104 GBV_ILN_121 GBV_ILN_123 GBV_ILN_130 GBV_ILN_150 GBV_ILN_151 GBV_ILN_179 GBV_ILN_184 GBV_ILN_216 GBV_ILN_267 GBV_ILN_293 GBV_ILN_754 GBV_ILN_2021 GBV_ILN_2024 GBV_ILN_2031 GBV_ILN_2046 GBV_ILN_2057 GBV_ILN_2060 GBV_ILN_2065 GBV_ILN_2170 GBV_ILN_2412 GBV_ILN_2415 GBV_ILN_2505 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4125 GBV_ILN_4247 GBV_ILN_4261 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4311 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4321 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 44 2020 3 30 11 516-529
allfieldsSound	10.1515/bfp-2020-2042 doi (DE-627)OLC2142166024 (DE-B1597)bfp-2020-2042-p DE-627 ger DE-627 rakwb 000 010 030 050 060 070 080 370 700 VZ 020 VZ 24,1 ssgn BBI DE-23 fid Auer, Sören verfasserin aut Improving Access to Scientific Literature with Knowledge Graphs 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © 2020 Walter de Gruyter GmbH, Berlin/Boston 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. Oelen, Allard aut Haris, Muhammad aut Stocker, Markus aut D’Souza, Jennifer aut Farfar, Kheir Eddine aut Vogt, Lars aut Prinz, Manuel aut Wiens, Vitalis aut Jaradeh, Mohamad Yaser aut Enthalten in Bibliothek De Gruyter, 1977 44(2020), 3 vom: 30. Nov., Seite 516-529 (DE-627)12961193X (DE-600)243153-1 (DE-576)015107671 0341-4183 nnns volume:44 year:2020 number:3 day:30 month:11 pages:516-529 https://doi.org/10.1515/bfp-2020-2042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BBI SSG-OLC-BUB SSG-OLC-SPO SSG-OPC-BBI GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_50 GBV_ILN_61 GBV_ILN_70 GBV_ILN_74 GBV_ILN_90 GBV_ILN_104 GBV_ILN_121 GBV_ILN_123 GBV_ILN_130 GBV_ILN_150 GBV_ILN_151 GBV_ILN_179 GBV_ILN_184 GBV_ILN_216 GBV_ILN_267 GBV_ILN_293 GBV_ILN_754 GBV_ILN_2021 GBV_ILN_2024 GBV_ILN_2031 GBV_ILN_2046 GBV_ILN_2057 GBV_ILN_2060 GBV_ILN_2065 GBV_ILN_2170 GBV_ILN_2412 GBV_ILN_2415 GBV_ILN_2505 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4125 GBV_ILN_4247 GBV_ILN_4261 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4311 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4321 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 44 2020 3 30 11 516-529
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title_auth	Improving Access to Scientific Literature with Knowledge Graphs
abstract	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. © 2020 Walter de Gruyter GmbH, Berlin/Boston
abstractGer	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. © 2020 Walter de Gruyter GmbH, Berlin/Boston
abstract_unstemmed	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. © 2020 Walter de Gruyter GmbH, Berlin/Boston
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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. 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Improving Access to Scientific Literature with Knowledge Graphs

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