Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2007 Oct;40(5):582-602.
doi: 10.1016/j.jbi.2007.03.005. Epub 2007 Mar 27.

Conceptual knowledge acquisition in biomedicine: A methodological review

Affiliations
Review

Conceptual knowledge acquisition in biomedicine: A methodological review

Philip R O Payne et al. J Biomed Inform. 2007 Oct.

Abstract

The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.

PubMed Disclaimer

Figures

Figure 1
Figure 1. Key components of the KE process
Components that are involved in the conceptual KA sub-process of KE are shaded. (Adapted from Liou, “Knowledge Acquisition: Issues, Techniques, and Methodology”, 1990)
Figure 2
Figure 2. Spectrum of knowledge types
(Adapted from McCormick, “Conceptual and Procedural Knowledge”, 1997)
Figure 3
Figure 3. Literature search results for phrases intended to retrieve articles pertinent to the KE domain, drawn from biomedical (PubMed), computer science (ACM), psychology and cognitive science (PsycARTICLES), and education (ERIC) literature databases
Figure 4
Figure 4. Overview of methodology employed for literature review process. Labels in italics indicate article selection criteria subject to iterative refinement
Figure 5
Figure 5. Overview of psychology-based theoretical model of expert knowledge transfer
Implicit in this model is the ultimate unification of the psychology of the person (expert knowledge source) and the ontology (conceptual knowledge collection) of the computer. (Adapted from Gaines and Shaw, “Knowledge Acquisition Tools Based On Personal Construct Psychology”, 1993)
Figure 6
Figure 6. Hawkins model of expert-client knowledge transfer
In this model, the client elicits advice and data from the expert, which are in turn formulated and applied by the expert via a pre-existing knowledge model. (Adapted from Gaines, “Social and Cognitive Processes in Knowledge Acquisition”, 1989)
Figure 7
Figure 7. Ogden-Richards semiotic triad, illustrating the relationships between the three major semiotic-derived types of “meaning”
Figure 8
Figure 8. Organizing taxonomy of KA techniques, composed of three primary categories: knowledge unit elicitation, knowledge relationship elicitation and combined elicitation, which includes techniques that incorporate aspects of both of the preceding categories
Figure 9
Figure 9. Example of a basic repertory grid eliciting relationships between treatment options (elements) and various decision-making metrics (constructs)
For each element in the grid, the expert completing the grid provides a numeric score using a prescribed scale (defined by a left and right pole) for each distinction, indicating the strength of relatedness between the given element-distinction pair. In many instances, the description of the distinction being used in each row of the matrix is stated differently in the left and right poles, providing a frame of reference for the prescribed scoring scale.
Figure 10
Figure 10. Differentiation of types of agreement in multi-expert KA studies
In this model, the use of the “same” nomenclature or distinctions refers to the sources or experts using semantically similar or compatible means of describing or classifying concepts in a domain. Similarly, the use of “different” nomenclature or distinctions refers to the sources or experts using semantically dissimilar or incompatible means of describing or classifying concepts in a domain. (Adapted from Gaines and Shaw, “Knowledge Acquisition Tools based on Personal Construct Psychology”, 1993)
Figure 11
Figure 11. Proposed taxonomy of verification and validation metrics for conceptual knowledge collections
Figure 12
Figure 12. Overview of information theoretic evaluation method for determining the degree of multi-source or expert agreement within a knowledge collection or system
Figure 13
Figure 13. Verification and validation ontology, composed of three axes
Axis 1 defines the type of evaluation being performed (e.g., verification or validation). Axis 2 defines both major criteria of interest to be evaluated, as well as any applicable sub-types. Axis 3 defines the methods that may be applied to measure the criteria of interest. The connections between members of each axis indicate the applicable verification and validation scenarios that combine an evaluation type, criteria and method.

Similar articles

Cited by

References

    1. McCormick R. Conceptual and Procedural Knowledge. International Journal of Technology and Design Education. 1997;7:141–159.
    1. Achour SL, et al. A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development. J Am Med Inform Assoc. 2001;8(4):351–60. - PMC - PubMed
    1. Ashburner M, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. - PMC - PubMed
    1. Bakken S, et al. Evaluation of the clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures. J Am Med Inform Assoc. 2000;7(6):529–38. - PMC - PubMed
    1. Bell DS, Pattison-Gordon E, Greenes RA. Experiments in concept modeling for radiographic image reports. J Am Med Inform Assoc. 1994;1(3):249–62. - PMC - PubMed

LinkOut - more resources