
2 Arab Oghli et al.
a crucial machine-actionable unit of scholarly content in the form of human and
machine-readable comparisons of semantified scholarly contributions [44]. These
comparisons are meant to be used by researchers to quickly get familiar with
existing work in a specific research domain. For example, determining the repro-
duction number estimate R0 of the Sars-Cov-2 virus from a number of studies
in various regions across the world https://orkg.org/comparison/R44930. The
semantically represented scholarly contribution comparisons in ORKG are espe-
cially necessary in our era of the deluge of peer-reviewed publications [29] and
preprints [18] to help researchers stay on top of the fast-paced scientific progress.
It concretely helps scientists to still keep an oversight over scientific progress by
freeing unnecessary human cognitive tie-ups involved when searching for key
information buried in large volumes of text.
The ORKG machine-readable comparisons depend on the availability of a
knowledge base of machine-actionable, semantified scholarly contributions. The
scholarly contributions are a unit of information defined in the context of the
ORKG that describe the addressed problem and comprise the utilized materials,
employed methods and yielded results in a scholarly article – a model which sub-
sumes Leaderboards [27,31]. A large community of researchers has recently
been growing around the crowdsourced curation of scholarly contributions in
the ORKG (e.g., https://orkg.org/paper/R163747).1To describe the scholarly
contributions, RDF statements are used as structured semantic units that are
machine-actionable as a result. A core semantic construct of these contribution-
centric statements are the predicates or properties used to describe the contri-
bution of an article. While the subject and object are content-based, predicates
can generically span contributions across articles. E.g., task name,dataset name,
metric, and score are a group of four predicates used to semantically describe
the leaderboard contribution across AI articles [31] in the Computer Science
domain; the predicates basic reproduction number,confidence interval (95%),
location, and time period are used to describe Covid-19 reproductive number
estimates in epidemiology articles [43].
Predicates are a core construct for semantically describing contributions in
ORKG. To base the ORKG on meaningfully described semantic scholarly contri-
butions, certain, specific groups of predicates that can capture key contribution
aspects of the scholarly articles are essential. Each such group then becomes a
contribution-centric predicate group. Further, the group varies in applicability
from being applicable to only a specific scholarly contribution or generalizing
across a group of contributions from different papers. In this respect, the ORKG
follows an agile, iterative Wiki-style collaboration approach giving curators the
autonomy to coin new properties easily, but aims in the long-term trajectory
to be coherent in terms of vocabulary for both predicates and resources. Note
that contributions can only be compared based on standard predicates terminol-
ogy for the machine-readable ORKG comparisons. Further, the typical lifecycle
of a new KG construction must also be accounted which starts with nascent
1The related construct to ORKG contributions, of LeaderboardsinAIhttps:
//paperswithcode.com/ has also garnered large-scale crowdsourcing interest.