Knowledge structures

The extent and complexity of meanings we hold in any domain are dependent on the quality and quantity of meaningful learning we have pursued in that knowledge domain. In turn, the quantity and quality of the knowledge structures we build will determine our ability to transfer this knowledge for use in new contexts. 

Since the aim of this chapter is to provide a general outlook on interdisciplinarity in hybrid nanomaterials, we have focused our efforts on the knowledge structure and briefly touched upon the drivers for integration, both of which can be explored using bibliographic data. An exploration of the interdisciplinary practices (e.g., collaborations or recruitment) falls beyond the scope of this investigation. 

Figure 24.1 Schematic representation of the methodology employed to map the knowledge structure of hybrid nanomaterials research. See Rafols and Meyer25 for details.

Figure 24.2 Knowledge structure of hybrid nanomaterials research. The nodes of the network represent review publications. The labels indicate first author and year of publication. The lines represent the similarity between nodes, as measured by the normalized number of shared references (bibliographic coupling).

Table 24.1 Description of Central Topics in the Clusters of the Knowledge Structure of Hybrid Nanomaterials, According to Bibliographic Coupling Networks (see Fig. 24.2)...

Brown, David C., and B. Chandrasekaran. Design Problem Solving Knowledge Structures and Control Strate es, Pitman, London and Morgan Kaufman, San Mateo, CA (1989). 

Minsky s theory articulates the components of the frame. An important contribution to the development of schema theory as we understand it today is Minsky s (1975) recognition of anticipatory knowledge in the knowledge structure. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed (p. 212). Not only is there a component dealing with anticipation, but there is also knowledge about how to take action, reminiscent of Piaget s theory. 

Let us turn now to a different question, namely Do individuals actually have knowledge structures that function as the above theorists have suggested Pieces of confirming evidence can be found in Bartlett s and Piaget s studies. Both of these researchers attempted to establish the existence of schemas through empirical study. 

Rumelhart et al. offer a much narrower view of a schema than the one taken here. Indeed, what they call a schema hardly differs from a concept. For example, they developed a room model to illustrate schemas in a PDP representation. The model learns to recognize five rooms based on forty descriptors such as toaster, bathtub, television, and so on. The model learns to associate particular features with particular rooms, and in fact it develops concepts for the five rooms. Nevertheless, the knowledge structure built by the model lacks many of the critical features of a schema as outlined in chapters 2 and 3. 

Almost certainly, the philosophical debate about the nature of schemas belongs at the most abstract level. In general, philosophers have not been concerned with a particular context in which schemas are used but rather with whether such knowledge structures exist at all. The method of study is largely reflection or introspection. 

Marshall, S. P., Pribe, C. A., Smith, J. D. (1987). Schema knowledge structures for representing and understanding arithmetic story problems (Tech. Rep. 87-01, ONR Contract N-000140-K-85-... 

A simple keyword-based IR approach greatly limits the exploitation of the knowledge structure contained in returned documents. Clustering provides a major improvement in the grouping and prioritization of a set of documents. Clustering arose from a need to improve IR systems [23,24], identify similar documents [25], and better organize and browse a group of documents [26,27]. 

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