Knowledge Organisation: an LIS (library and information science) perspective

15.1Knowledge Organisation: an LIS (library and information science) perspective

(Friedman and Smiraglia, 2013) seek to

“improve comprehension of the socially-negotiated identity of concepts in the domain of knowledge organization. Because knowledge organization as a domain has as its focus the order of concepts, both from a theoretical perspective and from an applied perspective, it is important to understand how the domain itself understands the meaning of a concept. ” (Friedman and Smiraglia, 2013).

They do so within the frame of what they mean by Knowledge Organisation KO, which is identified as the:

“entire population of formal proceedings in knowledge organization – all proceedings of the International Society for Knowledge Organization’s international conferences (1990-2010) and those of the annual classification workshops of the Special Interest Group for Classification Research of the American Society for Information Science and Technology (SIG/CR).” (Friedman and Smiraglia, 2013).

Thus knowledge organization KO is defined and identified with the perspective adopted by ISKO and its journal, the Journal of Knowledge Organization. Most writers in this field come from the LIS Library and Information Science discipline. Their perspective is very important but subtly different both from the information systems discipline and the cognitive science approaches.

The literature on knowledge organisation makes frequent reference to knowledge organisation systems KOS. Although I instinctively dislike this term because the literature surrounding it does not always appear to understand what a system is, I will use it because others do.

§16Positioning Conceprocity among Knowledge Organisation Systems

16.1Knowledge Representation (KR) as the primary dimension for classifying and comparing Knowledge Organisation Systems KOS

They themselves use a CMap concept map (cf. (Novak and Cañas, 2008)) to suggest a tentative set of types of KOS (Rocha Souza, Tudhope and Barcellos Almeida, 2010). This is reproduced here as Figure :

Figure A tentative set of types of KOS (from Rocha Souza et al., 2010, FIG 1)

Conceprocity could appear at more than one point in this essentially hierarchic classification, since it can be used as a form of concept mapping system, but it can also be used to make a taxonomy or classification scheme (Jacob, 2004) and in the construction of semi-formal ontologies.

(Rocha Souza et al., 2010: figure 3) reproduces a KOS Spectrum originally proposed by (Daconta, Obrst and Smith, 2003). It is repeated here as Figure :

Figure KOS Spectrum Source: (Daconta, Obrst and Smith, 2003)

This uses a single dimension, identified as semantic strength, along which they position various KOS. (Rocha Souza, Tudhope and Barcellos Almeida, 2010) comment that (Daconta, Obrst and Smith, 2003) tend to present KOS and their representational languages together; but we would comment that they themselves tend to identify and equate KR and KOS as we have previously seen in Figure . Here in Figure , travelling from weak semantics to strong is identified with the formality of the knowledge representation language.

Conceprocity has rather weak semantics. It can be used to represent data structures in a way which is as precise as the ER Entity Relationship model and indeed Chen’s E/R Entity Relationship model (Chen, 1976).

Conceprocity is in fact considerably more expressive than even an extended ER model, which concerns itself with data and its semantics. Conceprocity recognises procedures, logical connectors, principles and events. However the semantics associated with the event and process elements are deliberately not very precise since they depend on the modeller’s use of natural language text. It is possible to represent relational data structures, processes, events and Boolean decisions in Conceprocity but no pretence is made to sufficiently formal semantics to permit, for example, the execution of a Conceprocity model – it is not a programming language and does not support automatic inferencing. In any concept mapping approach, the precise form of the name given to a concept and to a relationship – expressed as they are in natural language – are at one and the same time extremely important and extremely difficult to get right. We present Conceprocity as a visual language and intend it to be used for communication between human observers.

(Rocha Souza, Tudhope and Barcellos Almeida, 2010) quote (Guarino, 2006) as proposing the term « ontological precision ». They reproduce a figure from Nicola Guarino as their FIG. 8, reproduced here as Figure :

Figure Levels of ontological precision - (Guarino, 2006)

(Guarino, 1998) had earlier clarified the terminology associated with ontology as a philosophical term versus ontology as an artefact in the following terms:

Ontology: the philosophical discipline

Study of what there (possibly) is

Study of the nature and structure of reality

Domain of entities

Categories and relations

Characterizing properties

An ontology: a theoretical or computational artefact

“An explicit and formal specification of a conceptualization” (Gruber, 1993)

A specific artefact expressing the intended meaning of a vocabulary in terms of the nature and structure of the entities it refers to

In its current form, Conceprocity can be used to represent taxonomies and informal ontologies.

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