KGMM - A Maturity Model for Scholarly Knowledge Graphs Based on Intertwined Human-Machine Collaboration

Hussein, Hassan; Oelen, Allard; Karras, Oliver; Auer, Sören

doi:10.1007/978-3-031-21756-2_21

Cited by 5 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, subject librarians completed a survey evaluating the KGs on a research topic from within their domain of expertise. This survey elicited feedback relevant to establishing a KG's maturity level, as defined in Hussein et al (2022). Third, the same subject librarians assessed whether they believed the given KG would be useful for informing either a domain expert or a layperson on the research theme.…”

Section: Discussionmentioning

confidence: 99%

“…The Knowledge Graph Maturity Model (KGMM) offers an assessment of data in KGs with the goal of offering it in "the most mature, complete, representable, stable, and linkable shape" (Hussein et al 2022). The model itself distinguishes five maturity levels, with each level possessing its own set of quality measures that are ranked in priority as either essential, important, or useful.…”

Section: Knowledge Graph Maturity Modelmentioning

confidence: 99%

“…KGMM (Hussein et al 2022) was discussed in theory to apply to KGs generally, but also included a proposed implementation specifically for ORKG Comparisons. All measures that were not either directly satisfied by the ORKG publishing model or which did not have a mechanism in the ORKG based on which they could be satisfied, were proposed as a survey questionnaire.…”

Section: Qualitative Evaluation Criterion 2 -Knowledge Graph Maturity...mentioning

confidence: 99%

“…The paper is structured as follows. Section 2 discusses related work on KG quality evaluations and introduces the Knowledge Graph Maturity Model (KGMM) (Hussein et al 2022) used for criterion 2. Section 3 introduces the ORKG and its features, in particular the ORKG Comparisons analysed in this work.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quality Assessment of Research Comparisons in the Open Research Knowledge Graph

D'Souza,

Hussein,

Evans

et al. 2024

JLIS.it

Self Cite

View full text Add to dashboard Cite

The Open Research Knowledge Graph (ORKG) is a digital library for machine-actionable scholarly knowledge, with a focus on structured research comparisons obtained through expert crowdsourcing. While the ORKG has attracted a community of more than 1,000 users, the curated data has not been subject to an in-depth quality assessment so far. Here, proposed as a first exemplary step, within a team of domain experts, we evaluate the quality of six selected ORKG Comparisons based on three criteria, namely: 1) the quality of semantic modelling, 2) the maturity of the Comparisons in terms of their completeness, syntactic representation, identifier stability, and their linkability mechanisms ensuring the interoperability and discoverability. Finally, 3) the informative usefulness of the Comparisons to expert and lay users. We have found that each criterion addresses a unique and independent aspect of quality. Backed by the observations of our quality evaluations presented in this paper, a fitting model of knowledge graph quality appears one that is indeed multidimensional as ours.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Knowledge Graph Maturity Modelmentioning

confidence: 99%

Section: Qualitative Evaluation Criterion 2 -Knowledge Graph Maturity...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quality Assessment of Research Comparisons in the Open Research Knowledge Graph

D'Souza,

Hussein,

Evans

et al. 2024

JLIS.it

Self Cite

View full text Add to dashboard Cite

show abstract

“…We developed an ORKG template 3 to organize the scientific data extracted from the papers in the ORKG. ORKG templates implement a subset of the Shapes Constraint Language (SHACL) and allow specifying the underlying (graph) structure to organize the data in a structured manner [11]. In this way, we determined which data to extract and standardized their description to ensure they are FAIR, consistent, and comparable across all papers.…”

Section: A Kg-empirementioning

confidence: 99%