Sketchbook

Associations express relationships between subjects by relating one topic to (zero or more)

other topics. They were originally meant to represent the ‘See also’ references that appeared in back-of-book indexes.

Each topic that participates in an association is said to play a role in the relationship that is expressed by the association. The nature of the subject’s involvement in a particular relation is expressed using a role type. e.g. Puccini plays the role of pupil in the teacher/pupil

relationship with Ponchielli. This mechanism obviates the need for associations to have a specific direction, and all associations are therefore inherently multidirectional.

2.5.7

Scope

Scope is a set of topics that is used to qualify a statement (i.e., a name, occurrence, or

association) with the purpose of indicating the context in which a certain assertion may be considered valid. If no scope is explicitly specified, the scope is said to be ‘unconstrained’. Topics that are used for scoping are informally referred to as ‘scoping topics’.

2.5.8

Merging

Merging is a process or operation and as such is different from the previous elements, which

are constructs in the Topic Maps model. Merging can take place both within a single topic map (to eliminate redundancy) and when combining two or more topic maps. This process lies at the core of the Topic Maps paradigm, and can be traced back to the original motivation (merging indexes) that gave rise to the paradigm. While merging is an operation performed by an application, its procedures are strictly defined in the standard and it is based on the concept of identity described above.

2.5.9

Reification

Reification is the process of instantiating as a topic some Topic Map construct (a name,

occurrence, association, role, or even the topic map itself) that itself is not a topic. Once this is done, whatever is represented by the construct in question becomes a subject in its own right, about which statements can be made. Reification is most often used to assign metadata to a topic map.

2.6

Knowledge Representation

2.6.1

General

According to Sowa (2000a), Knowledge Representation (KR) is an interdisciplinary field of study, derived as a branch of Artificial Intelligence (AI), which applies theories and

techniques from logic, ontology and computation (Sowa, 2000a, p. xi-xii). The principles of KR, according to the same author are five: a knowledge representation is a surrogate, is a set of ontological commitments, is a fragmentary theory of intelligent reasoning, is a medium for efficient computation, and is a medium of human expression (Sowa, 2000a, p.134).

KR is closely related to simulation of human reasoning to model it in a way that computers can “understand”, simulate it, and make inferences based on this. It is generally agreed that the main problem is that these processes take place inside human minds, and thus, their representation have to be based on things that only exist externally.

“Any intelligent entity that wishes to reason about its world encounters an important, inescapable fact: reasoning is a process that goes on internally, while most things it wishes to reason about exist only externally. A program (or person) engaged in planning the assembly of a bicycle, for instance, may have to reason about entities like wheels, chains, sprockets, handle bars, etc., yet such things exist only in the external world.” (Davis et al., 1993)

KR can take many forms and be applied in many fields and “things”. In LIS for instance, it is applied to the objects of the “bibliographic universe”, composed by documents which are at the same time representations of human creation and thought through the use of language (written, visual, graphic, acoustic). These documents are at the same time represented, through the use of a “bibliographic language” in “bibliographic descriptions” arranged in “bibliographic systems” or, “knowledge representations”, as called by Svenonius (2004). However, although some of the mentioned principles of KR could be applied to bibliographic languages and lead us think that those could be knowledge representations, the scope or purpose of KR compared to that of Information Organization and/or KO is broader and couldn’t be considered as the same in those two disciplines. For instance, KR models events and operations (such as in the example presented by Sowa on the functioning of a system for

traffic lights), covers simulated behaviors (p.141), and tries to model logics and automated reasoning (p.4), using surrogates “to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it” (Davis, Schrobe & Szolovits, 1993), while bibliographic languages have a scope limited to the modeling of conceptual structures based on documents.

Perhaps what makes Information Organization and KO close to KR is the first principle of the latter one: “a knowledge representation […] is most fundamentally a surrogate, a substitute for the thing itself” (Davis, Schrobe & Szolovits, 1993), which could be a valid definition of

metadata.

In Svenonius (2000) view, there are other kinds of knowledge representations nowadays, e.g. lexical databases, metathesauri, semantic networks, etc. (p.146). However, some of them have been developed outside the realm of LIS, and have been applied in the domain of KR and in other fields. Since Topic Maps transcends the LIS realm, some considerations found in the literature, relate it to these other knowledge representations looking at their possibilities in the LIS realm. Two of them are recurrent: semantic networks and ontologies. Here it is a

conceptual explanation of both: