Details
| Original language | English |
|---|---|
| Title of host publication | JCDL 2020 |
| Subtitle of host publication | Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 107-116 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450375856 |
| Publication status | Published - 1 Aug 2020 |
| Event | 2020 ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2020 - Virtual, Online, China Duration: 1 Aug 2020 → 5 Aug 2020 |
Abstract
There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process that supports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.
Keywords
- Digital libraries, Fair data principles, Machine actionability, Ontology, Open science, Scholarly infrastructure, Semantic publishing
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
Sustainable Development Goals
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JCDL 2020 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 107-116 3398530.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Toward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells
AU - Vogt, Lars
AU - Souza, Jennifer D.
AU - Stocker, Markus
AU - Auer, Soren
PY - 2020/8/1
Y1 - 2020/8/1
N2 - There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process that supports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.
AB - There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process that supports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.
KW - Digital libraries
KW - Fair data principles
KW - Machine actionability
KW - Ontology
KW - Open science
KW - Scholarly infrastructure
KW - Semantic publishing
UR - http://www.scopus.com/inward/record.url?scp=85090909033&partnerID=8YFLogxK
U2 - 10.1145/3383583.3398530
DO - 10.1145/3383583.3398530
M3 - Conference contribution
AN - SCOPUS:85090909033
SP - 107
EP - 116
BT - JCDL 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2020
Y2 - 1 August 2020 through 5 August 2020
ER -