Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context

Abstract

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “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.

Interdisciplinarity of information science: an evolutionary perspective of theory application

“…Knowledge unit distribution (Xu et al, 2016;Wang et al, 2022), citation locations (Wang et al, 2021a, b) and coword network (Ba et al, 2019;Engerer, 2017) are used to characterize the diffusion, transfers and integration of knowledge among different disciplines and thus to reveal the interdisciplinarity of the research. Currently, knowledge units are mainly represented with terms, knowledge memes, keywords and topics contained in the publications (Wang et al, 2021a, b;Ba et al, 2019;Engerer, 2017;Li et al, 2021;Cheng et al, 2020;Mao et al, 2020), focusing on revealing the knowledge content in the research and expressing the details of knowledge interaction between different disciplines from a micro perspective. However, the discipline to which a knowledge unit belongs is often defined according to the discipline categories provided by the database, which may result in a bias in interdisciplinarity measurement for two reasons: First, there may be differences between the classification of subject topics in the scientific databases (e.g.…”

The confirmation of scientific theories using Bayesian causal networks and citation sentiments

“…The so-called "semantic predications" are available through the NLM's SemMed database and have been used by to map the subject and object connections involving causal type links in various ways to understand how causal connections can transform biomedical research areas. In a similar vein Li, Peng, and Du (2021b) have explored SPO triples as knowledge units in connection with the uncertainty sentiment as part of a case study of lung cancer.…”

Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

“…Uncertain scientific knowledge refers to the knowledge originating from hypothetical, speculative statements or even conflicting and contradictory assertions. It is critical to understand the incremental and transformative development of scientific knowledge (Chen, Song, & Heo, 2018;Li, Peng, & Du, 2021;Small, 2020). For example, it is common in medical research or clinical practice to encounter medical reversals, with contradictory results in subsequent research.…”