  In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept
"knowledge unit" and "knowmetrics" for measuring knowledge. However, the
definition of "computable knowledge object" remains controversial so far in
different fields. For example, it is defined as 1) quantitative scientific
concept in natural science and engineering, 2) knowledge point in the field of
education research, and 3) semantic predications, i.e.,
Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic
MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples
extracted from medical literature, provides a basic data infrastructure for
measuring medical knowledge. In general, the study of extracting SPO triples as
computable knowledge unit from unstructured scientific text has been
overwhelmingly focusing on scientific knowledge per se. Since the SPO triples
would be possibly extracted from hypothetical, speculative statements or even
conflicting and contradictory assertions, the knowledge status (i.e., the
uncertainty), which serves as an integral and critical part of scientific
knowledge has been largely overlooked. This article aims to put forward a
framework for Medical Knowmetrics using the SPO triples as the knowledge unit
and the uncertainty as the knowledge context. The lung cancer publications
dataset is used to validate the proposed framework. The uncertainty of medical
knowledge and how its status evolves over time indirectly reflect the strength
of competing knowledge claims, and the probability of certainty for a given SPO
triple. We try to discuss the new insights using the uncertainty-centric
approaches to detect research fronts, and identify knowledge claims with high
certainty level, in order to improve the efficacy of knowledge-driven decision
support.
