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Conceptual
(Declarative)
Knowledge
Strategic
Knowledge
Procedural
Knowledge
Problem Solving Process
Fig. 8.3 Spectrum of knowledge types, spanning from conceptual to strategic to procedural
knowledge, where conceptual knowledge is the most abstract form of understanding a domain, and
procedural knowledge is the most application- or problem-oriented understanding of a given need
or task
In the following sections, we will explore critical aspects of all three of the afore-
mentioned foundational dimensions that underpin the design and use of in silico
hypothesis discovery tools and platforms.
8.2
Conceptual Knowledge in Biomedicine
Conceptual knowledge has been defi ned in the computational, psychology, and edu-
cation literature as being comprised of a combination of atomic units of information
and the meaningful relationships between those units. The same literature goes on
to defi ne two additional types of complementary knowledge, known as procedural
and strategic knowledge respectively. Procedural knowledge is a process-oriented
understanding of a given problem domain [ 17 - 20 ], effectively concerned with the
methods and approaches used to solve a given problem or address a task. Strategic
knowledge is that which is used by individuals in order to translate conceptual
knowledge into procedural knowledge [ 19 ] (Fig. 8.3 ).
Of note, these defi nitions are based upon a wide-ranging collection of empirical
research on learning and problem-solving in complex scientifi c and quantitative
domains such as mathematics and engineering [ 18 , 20 ]. The cognitive science lit-
erature provides a very similar and confi rmatory differentiation of knowledge types,
making the distinction between procedural and declarative knowledge. Declarative
knowledge in this context is synonymous with conceptual knowledge as defi ned
previously [ 21 ].
Conceptual knowledge collections in the biomedical domain include a variety
of constructs such as ontologies, controlled terminologies, semantic networks and
database schemas. A common theme when considering the existing state-of-the-art
relative to the design and use of conceptual knowledge collections in the biomedical
domain is the need for systematic and rigorous processes for representing concep-
tual knowledge in a computable form. It is also important to note when consid-
ering the need for such knowledge representation best practices that conceptual
knowledge collections rarely exist in isolation. Instead, they usually occur within
structures that contain multiple types of knowledge. For example, a modern clinical
decision support system (CDSS) might include: (1) a database of potential fi nd-
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