5.4 Practical cognitive terminology
5.4.1 Knowledge engineering
Knowledge engineering is a common term associated with the subfield of artificial intelligence
that is concerned with “transferring expert human knowledge to the system” (Franklin, 1995, p. 277). As such, it can be seen as a quasi-synonym for knowledge representation, although knowledge engineering is apparently more concerned with the conceptual entities themselves,
whileknowledge representation seems more concerned with their structure:
Typically, work inknowledge representation focuses either on the representational formalism or on the information to be encoded in it, sometimes called knowledge engineering. [... T]he central topic in knowledge engineering is to identify an ap-
propriate conceptual vocabulary; a related collection of formalized concepts is often
called an ontology.
Hayes, 1999, 432, my emphasis
This aspect is rarely problematic as it can be seen to collapse into the practices of generic terminography. This seems to be broadly recognized:
A typical knowledge engineers work [sic!, PBN] in many ways like terminologists.
For instance, they collect and catalogue the so-called domain entities in a manner that is similar to the building of glossaries or term lists. [...] Some knowledge engi- neers drawcomplex hierarchies in which objects are organized ininheritancenetworks
and/or partonymies, a process that resembles the development of conceptual struc- tures in terminology management.
Ahmad, 2001, 821, my emphasis
An example for an informal ontology can be found in Figure 4.3, which should however be seen through the lens of second-order cybernetics: the system is not the model. In terms of
the terminology used, the schema – a term that like the adjective schematic has been used
frequently so far – is not identical to the scheme. The schema we have presented has been
designed so that a variety of conventional models or entities (worldview, thick concept, meta- scheme, scheme, text-world model, discourse-world model), goal structures (theory, principle, approach, procedure) and actions or process concepts (control, abstract, extrapolate) could be
bound together and structured in a coherent visual model. This was subsequently “translated” into a coherent stretch of text. Our model is hypothetical and speculative, and it would amount to a statement of indefensible nonsense if we claimed that our model really represents what actually is inside – even the present author’s, much more other people’s – mind. In other words,
theschema is not thescheme.
Our embodied worldview cannot literally be transferred to the diagram, the written page, or
any other artifact; at best, we might suggest a possible – i.e. viable – explanation based on
processes of introspection and experimentation. Both of these processes are in turn based on
mind. From this point of view of “the transferring [of] expert human knowledge to the system” becomes problematic. It can hardly be considered surprising “that only rudimentary levels of expert behavior can be captured by explicit rules and facts” (Dreyfus, cited in Riegler, 2007).
While practitioners of artificial intelligence can likely deal with this problem or have an intuitive grasp of it, interpretations of terms and practices likeknowledge engineering become precarious
when they begin to travel beyond their fields by mechanisms ofimport orinter-domain borrowing.
An example for this can be found in the visionary, though little known10paper “Terminology vs. Artificial Intelligence” by Paul Wijnands (1993). Its author anticipated a few aspects that have found treatment or reception in terminology research only a few years later in the wake of the “computational”, “sociological” and “cognitive turns” of the discipline (Gerhard Budin, in Antosik, 2013) as well as through the emergence of sociocognitive terminology. Among them are, e.g., the study ofprototypeconcepts (ibid, 169) – which relates to the first sense ofcognitive terminology –
and the difficulty of applying traditional classification systems to problems ofontology engineering
– another synonym for knowledge engineering and knowledge representation – which relates to
the above.
The gist of the argument there is that terminology research as a discipline needs to approach artificial intelligence both conceptually and practically, and vice versa. The interesting point, however, is the understanding of artificial intelligence which seems to underlie the following
claim:
By means ofentirely automatic processes, artificial intelligence aims to simulate human intelligence as it manifests itself in theunderstanding of all reality,concrete or abstract, with which human beings are confronted.
Wijnands, 1993, 166, my emphasis
This understanding overstates a mostly discredited view of artificial intelligence’s paradigm (see, e.g. Manteuffel (1992) for an early critical assessment) and thus provides some support for our constructivist-inspired idea of interpretation ashuman “meta-conceptual simulation” or the
partial explicitation ofworldview construction. How can this be explained?
What seems to have entered Wijands’ worldview as a central tenet is the idea of cognitivism,
which holds that the human mind is essentially an information-processing machine (Riegler,
2007). If this metaphor was literally correct, then not only the worldview itself, but also all
cognitive mechanisms to build it could be replicated by the techniques ofknowledge engineering
or knowledge representation; however, it has been observed that only “rudimentary levels of
expert behavior can be captured by explicit rules and facts”. This is an assessment that is also shared by practicing terminographers:
[W]hile the final results of terminology research may be based on knowledge, they arenot systematically encoded as knowledge. Rather, most of the subject-field knowl-
edge so laboriously acquired by the terminologist unfortunately stays where it was first stored, namely in the terminologist’s head. Only fragments of it are retained in definitions or examples[.]
Meyer, Eck, and Skuce, 1997, p. 98
These aspects of knowledge engineering point to a different problem, i.e. thehuman-computer dichotomy, whose detailed treatment must be relegated to further research insofar as it cannot be
10
As of my writing this (23 June 2013), Google scholar showed about 3 (!) citations for the article and about 31 for the edited book in which it was published.
construed as generic function of divergingagendas (7), codification mentalities (8) or any other
of the more preliminary considerations in the present work. Insofar as knowledge engineering is an aspect of philosophical terminography, its obvious application would be in concept structuring tasks like the exemplary effort in (4.3). We are however not concerned with the principles of
knowledge engineering, but with itsprocedures. This means that we select existingprocedures and assimilate them to our ownprinciples in terms of the praxeology ofphilosophical terminography.
Even considering that an inferential algorithm for the construction of maker’s knowledge and
a worldview11 is out of reach12,knowledge engineering nevertheless has a place inphilosophical terminography. For one, we use knowledge engineering products like semantic networks (Word-
Net; see (3.2)) as aids for reasoning about conceptual relations and interpreting philosophical
terms. As Meyer et. al. above suggest, such products can be seen as being based on knowl- edge, even though they cannot transmit knowledge. Secondly, we use a theory of mind for the
purpose of constructing speculations about the wider social reality. This action may be un- derstood in terms of simulation, albeit not quite in terms of computational simulation. With
regard to both aspects, the considerations start with the limits of their projected viability or
“the constraints within which equilibrium can be maintained” (Glasersfeld, 2002). In terms of interpreting linguistic meaning, “semantic compatibility” can also be seen as a “fitting within constraints”, (Glasersfeld, 1992).
5.4.1.1 Theory of mind
In this sense, discourse production is analogous toinformation production, which is assumed in
what could be considered the constructivisttheory of mind:
The branch of cognitive science that concerns our understanding of the minds of ourselves and others has come to be called “theory of mind,” though it should perhaps be called “theory of theory of mind.” It involves psychological theorizing about our ordinary, intuitive, “folk” understanding of the mind.
Gopnik, 1999, p. 838
Given thatthick concepts like scientificity anddisciplinarity and their adjunct schemes of text- world model and discourse-world model are supposed to control the production of discursive
cognitive artifacts, it is essential to understand that exercising the meta-scheme component
of the thick concepts presupposes an understanding of other minds – or rather the ability to
construct internal models of them – on the part of the human terminographer and discourse producer. It is this essentially human feat that exceeds the capabilities of formal encoding and machine simulation.
11Telling in this regard is the term microworld: “Philosophers often build their ontologies from the top down
with grand conceptions about everything in heaven and earth. Programmers, however, tend to work from the bottom up. For their database and AI systems, they often start with limited ontologies or microworlds, which have a small number of concepts that are tailored for a single application”, Sowa, 1999.
12
This also holds for the prospects of a fully automatic, high quality generation of discourse; readers are referred to http://www.elsewhere.org/pomo/for their amusement. With regard to the possibility ofsimulating the human intellect, Weizenbaum (1976, pp. 213/214) noted: “There is, however, still another assumption that information-processing modelers of man make that may be false, and whose denial severely undermines their program: that there exists one and only one class of information processes, and that every member of that class is reducible to [...] information process[ing ...] formalisms. Yet every human being has the impression that he thinks at least as much by intuition, hunch, and other such informal means as he does “systematically”, that is by means such as logic. Questions like “Can a computer have original ideas? Can it compose a metaphor or a symphony or a poem?” keep cropping up.”
In terms of our model, the human cognitive models orsimulations are deployed in the arranging
of text objects (res) and annotations (notae) and in the design (divisio) of the discursive artifact.
For selecting ourprocedures, we might as well look into how theprocess of simulation is thought
to work.
The constructivisttheory of mind13holds that knowledge relating to the social and experiential
reality a person inhabits is constructed along the lines of two principles: for one, the principle of goal-directedness (“Cognitive organisms do [...] develop attitudes towards their experience
because they like certain parts of it and dislike others. [...] human actions become goal-directed in that they tend to repeat likeable experiences and to avoid the ones that are disliked”, Glasersfeld, 1995, pp. 113/114) and secondly, the assumption that past experiences reflectregularities which
are bound to repeat (“One kind of knowledge [...] is knowledge of what has worked in the past and can be expected to work again”, ibid, 114).
Given that these principles are seen to be developmentally ingrained (based on “the Piagetian idea that some of the concepts that determine the structure of our experiental [sic, PBN] world are constructed during the sensorimotor period, prior to the age of 2 years, when we are anything but aware of what we are building”, ibid. 118), it follows that expectations regarding future
experiences are arrived at by projecting the recalled results of past experiences into the future (when “there is experiential knowledge of how to bring the desired end about[, t]his knowledge can be mapped as the re-presentation of an established cause-effect connection, and it is this re- presentation, projected into the future, that now becomes the cause of the activity that is believed to bring about the end”, Glasersfeld, 1990d; compare to definition ofscheme). Apparently, this
assumption follows from the second principle and loops back into the premise ofgoal-directedness.
We might take this as another application ofscheme theory if we compare it to our samples
and definition (4). It is at this third stage – the projection of past regularities into the future – that an actual act ofsimulation is assumed to take place. Here, a future scenario is arrived at
by fusing the abstracted regularities of past experience with the concrete expectations of future results on the basis of the experiential present. For this reason, we may categorize thistheory of mind in the category or family of theories which is subsumed by the term simulation theory of mind:
The simulation (or “mental simulation”) theory [... holds that] human beings are able to use the resources of their own minds to simulate the psychological etiology of the behavior of others, typically by making decisions within a “pretend” context.
13
The choice of this term to denote the necessary generic category can now seen as anauto-rhetoremic problem inphilosophical terminography since the termtheory of mind is never used by Ernst von Glasersfeld in the corpus of constructivism or in the main work, Glasersfeld 1995, not even inside the dedicated chapter (ch. 6, “Constructing Agents: The Self and Others”). The term simulation only occurs once together with a co- text that would allow such inferences: “Children at about the age of three can sometimes be observed to grab a small object, push it along the surface of the floor or table and accompany this movement with a sometimes remarkably well done imitation of engine noise [...] Even though this performance requires the transfer of conceptual properties [from one structure to another, PBN], I wouldn’t consider it a metaphor, but rather within the category of simulation. The child never believes the object to be an actual car, but chooses to regard it temporarily as such.”, Glasersfeld, 2006, my translation (Ger. “Kinder um das Alter von drei Jahren nehmen zuweilen einen kleinen Gegenstand in die Hand, schieben ihn auf dem Boden oder auf dem Tisch entlang und begleiten die Bewegung mit einer oft recht guten Imitation von Motorenlärm [...] Obgleich diese Vorführungen eine begriffliche Übertragung verlangen, möchte ich sie doch nicht als Metaphern bezeichnen, denn sie passen weit besser in die Kategorie der Simulation. Das Kind glaubt keineswegs, daß der sich bewegende Gegenstand ein Auto ist, doch es betrachtet ihn vorübergehend als Auto”). Case examples like this provide support for the viability of the experience-derived conventions of interpretativephilosophical lexicography. However, we can assert that an act ofinterpretation would not be initiated if not prompted by
A common method is role-taking, or “putting oneself in the other’s place.” [...] Sim- ulation is often conceived in cognitive-scientific terms: one’s own behavior control system is employed as a manipulable model of other such systems. The system is first taken off-line, so that the output is not actual behavior but only predictions or anticipations of behavior, and inputs and system parameters are accordingly not limited to those that would regulate one’s own behavior. [...] The simulation is [...] said to be process-driven rather than theory-driven (Goldman 1993)[.]
Gordon, 1999, p. 765
Insofar asprojection is concerned, the simulation involves the manipulation models of the envi-
ronment and others, which can be seen as “the resources” of the subject’s “own mind”. In terms of the system/ model distinction, a technical reformulation of the principle can be found in the statement that “veridical access epistemological access to reality is denied” (Ortony/ Temmer- man, (1.4.1)).
In terms ofprocedures that are based onsimulation, the most important one from the vantage
point of philosophical terminography is the construction of semantic networks, which could be
framed in terms of the constructivist theory of mind – hence, we assume that the semantic network aims to simulate the semantic associations (e.g. the “psycholexicological” approach of
WordNet, (3.2)) in ahypothetical speaker of the language whose vocabulary the semantic network
intends to represent. We have already attempted to exploit this simulation for the purpose of
inducing the kinds of stereotypes that might be ascribed to that hypothetical speaker (3) and
have therefore already become users of aknowledge engineering product. By contrast, we could
say that theconstruction of such artifacts is the province ofknowledge engineering proper.
5.4.1.2 Semantic network
We have hitherto frequently made use of WordNet, a tool that could be categorized as asemantic network ((1.4.1), (2.5.1), (3.2), (5.3)). Although a deeper problematization has not taken place
so far, we have discovered some of the limitations of reasoning with the help of such instruments experimentally. These limitations include, e.g. circularity and lack of context.
Given the – impossible – requirement that such knowledge representation products should
successfully simulateall experiential reality and thus “transfer” it to the computational system,
these networks could be considered unsuccessful implementations of atheory of mind. In practice,
however, this is not a flaw as the component of intelligence necessary for interpretation and knowledge construction will be provided by the human operator. Analogous to thesemasiological
use of thesauri to solve auto-rhetoremic problems (5.3), semantic networks help – due to their
logical, hierarchical structure – in approaching problems from the onomasiological perspective
(1.3.3). Asemantic network is a
Frequently used form of knowledge representation that uses agraph-like notation system. Originally developed to model associative memory, semantic networks have
evolved into general knowledge representation schemes. Semantic networks represent by using a hierarchy of concepts organized by a primitive relation such as ‘is A’ or ‘PART OR’. Further two-place relations (roles) are defined by using these. The
main task in developing semantic networks consists in establishing the inventory of semantic relations between concepts. Simple semantic networks are formally a restricted variant of predicate logic.
This is the opposite direction as seen from the semasiological approach that is suggested by
thesaurus use, given that not all general language thesauri incorporate a auxiliaryonomasiological
component such as we found in Roget’s (2000). For example, anyonomasiological component is
absent fromMoby’s thesaurus (2011), which however compensates for this shortfall by including
a much broader range of synonyms.
If we approach problems ofinterpretation anddiscourse production middle-out, then we would
need to make a relational use of either instrument. In this case, we would supplement thesema- siological perspective already built into WordNet by way of lexical relations (3.2) by supplying
additional synonyms from the alternative instrument. As the act of interpretation also requires the evaluation of discourse data – representing the “context” into which “conventional meanings” are inserted (4) – we need to account forprocedures to accomplish this feat as well.
As should emerge from the foregoing,philosophical terminography should be seen as asynthetic
approach toregenerative theory construction thatcombines all necessary inputs (theory,princi- ple, approach,procedure) and formulates their relations in a discursive artifact. This is strictly
speaking the domain of language engineering. However – as previously suggested – this under-
standing requires taking stock of the conventional extension of the concept as well as performing another action ofassimilation.