Requirements Analysis for an Open Research Knowledge Graph

Brack, Arthur; Hoppe, Anett; Stocker, Markus; Auer, Sören; Ewerth, Ralph

doi:10.1007/978-3-030-54956-5_1

Cited by 9 publications

(9 citation statements)

References 51 publications

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“…This SKG compares "routes" of networked users querying scientific information for discovery purposes and uses. Various studies in the literature (Aryani, Fenner et al, 2020;Brack, Hoppe et al, 2020) confirm that SKGs offer powerful means of representation of scholarly knowledge and assessment of research impact. This work will include applications of SKGs to RI uses.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Fabre

Egret

Schöpfel

et al. 2021

Quantitative Science Studies

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Research infrastructures (RI) offer researchers a multitude of research opportunities and services and play a key role in the performance, innovative strength and international competitiveness of science. As an important part of the generation and use of new knowledge and technologies, they are essential for research policies. Because of their strategic importance and their need for significant funding, there is a growing demand for the assessment of their scientific output and impact. Current research information systems (CRIS) contribute for many years now to the evaluation of universities and research organizations. Based on studies on the application of CRIS to infrastructures and on a recent French report on the scientometric assessment of RI, this paper analyses the potential of CRIS and their data models and standards (in particular the international CERIF format and the German RDC model) for the monitoring and evaluation of RI. The interaction between functional specificities of RI and standards for their assessment is outlined, with reference to their own potential to stimulate and share innovation in the networks located inside and outside RI. This societal challenge, more than an academic issue, is on the way to further harmonization and consolidation of shared and common RI metrics.

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Section: Discussionmentioning

confidence: 99%

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Fabre

Egret

Schöpfel

et al. 2021

Quantitative Science Studies

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show abstract

“…These criteria and methods cannot be applied in the schema fusion process. Coverage [32] and flexibility [33] are important criteria to evaluate the overlapping effect between schemas. In 2020, Giunchiglia and Fumagalli carried out a preliminary exploration on quantitative evaluation [34], but the evaluation method regarded the importance of all entity types as the same, without considering the differences among them.…”

Section: Related Workmentioning

confidence: 99%

Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

et al. 2021

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Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of each legacy database for the specific integration application rather than reusing the existing high-quality knowledge graphs. Consequently, a question arises as to whether the existing knowledge graph is compatible with cross-domain queries and with heterogenous schemas of the legacy systems. An effective criterion is urgently needed in order to evaluate such compatibility as it limits the quality upbound of the integration. This research studies the semantic similarity of the schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility, to evaluate the pairwise compatibility between the schemas. It takes advantage of the properties of knowledge graphs to evaluate the overlaps between schemas and defines the weights of entity types in order to perform precise compatibility computation. The effectiveness of the criteria obtained to evaluate the compatibility between knowledge graphs and cross-domain queries is demonstrated using a case study.

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“…For example, synthesizing research results from multiple research papers are accomplished by human authors in literature reviews. There is a research thread in the area of digital libraries and text mining that seeks to represent research processes and results in a machine-processable knowledge representation such as knowledge graph (Ehrlinger & Wöß, 2016;Gutierrez & Sequeda, 2019)-to support semantic information retrieval, customized information extraction, generation of literature overviews, reproduction of research results, and updating of systematic reviews (Brack et al, 2020;Slaughter et al, 2015). This emerging research area can be characterized as scientific knowledge graph.…”

Section: Introductionmentioning

confidence: 99%

Information structures in sociology research papers: Modeling cause–effect and comparison relations in research objective and result statements¹

Cheng

Khoo

2021

Asso for Info Science & Tech

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When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause-effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause-effect and comparison relations, and used as coding schemes to anno-

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Requirements Analysis for an Open Research Knowledge Graph

Cited by 9 publications

References 51 publications

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

Information structures in sociology research papers: Modeling cause–effect and comparison relations in research objective and result statements¹

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Requirements Analysis for an Open Research Knowledge Graph

Cited by 9 publications

References 51 publications

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

Information structures in sociology research papers: Modeling cause–effect and comparison relations in research objective and result statements1

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Information structures in sociology research papers: Modeling cause–effect and comparison relations in research objective and result statements¹