Conceptual modelling

Overview - what is the aim of the article? ”We build conceptual models in our heads to solve problems in our everyday life”… ”By learning about how concepts are represented in the mind, we should be able to improve our means to represent concepts externally, that is, in software” (p. 722) The focus is implementations and representations of concepts A universal model?

The article is mixing up two research areas: it is not always quite clear when it is explaining aspects of cognitive science, or when it is going into implementational issues of conceptual modelling related to computer science. This is obvious in the use of terminology in the article.

Propositional representation There exists a ”pure” thought before it is put into language Internal and external representation The relations between language and thought are subject to discussion (Sapir-Whorf Hypothesis)

The concept of a concept- basic terminology Distinguishes between two sorts of concepts: mathematical concepts and natural concepts Has the article left the ”propositional representation”-approach for a more language-oriented semantic approach here?

3 views The classical view The probalistic (prototype) view The exemplar view Descriptions of ways to classify Focused on implementational issues more than cognitive issues

The classical view A concept is defined by a list of necessary and sufficient properties: a summary description Well suited for mathematical concepts, but not for natural concepts

Limitations of the classical view Most natural concepts do not have defining features There exists disjunctive concepts, which means that different properties are necessary/sufficient for different instances of the same concept It is not possible to indicate if an instance of a concept is a typical representative of the concept People use non-essential properties as a basis of categorization of objects People are unsure how to categorize ambiguous objects

The probabilistic (prototypical) view Every property is assigned a probability according to how typical it is for the concept On the basis of these probabilities it is possible to make semantic networks Question: Who are assigning these probabilities? Question: And in what context? Question: Is this a more objective way of deciding into which category to put an instance? :-)

The exemplar view There exist an exemplaric instance of a concept which all other instances are rated against and categorized in relation to Not very well described

Conclusion on the views A weak conclusion: that natural concepts are complex and can’t be captured in the classical view? ”This finding is particularly relevant to software reuse, whose goal is to provide generic solutions that work in many contexts” - but how?

Stability of concepts The properties of a concept varies according to which context the concept is used in The properties are defined from a criteria of relevance in the particular context This is a semantic problem - how the same word can have different meanings in different contexts The original approach turned upside down? Is this concerning the concepts or the instances?

Concept core Back to the notion of essential properties... ”Not all properties are relevant in all contexts, but the more contexts a property is relevant in, the more essential the property is” Question: How can you measure this?

Implementational issues Informational contents of features Feature composition and relationships between features Quality of features

Abstraction and generalization The generalization proces is extending the definition of the concept so that it covers all instances The generalization builds up the set of properties - the abstraction is filtering away the irrelevant information Not a universal sort of abstraction, but an abstraction with a focus - it removes properties in relation to a given context The article doesn’t distinguish between concepts and representation of concepts - otherwise it would be the representations that were generalized/abstracted

Conclusion ”Concepts can be regarded as natural modelling elements because they represent a theory about knowledge organization in the human mind”. Is this really the case? Are you convinced?

An alternative: a more pragmatic approach? The article have not taken into account how incredible fast people learn how the consensus of structuring is in a given domain - they quickly learn how to model things the accepted way in a given context Perhaps it makes more sense to see the process of classification as a cultural and context-dependent activity? Perhaps the features of modelling have more in common with the semantics of natural languages than with the propositional representation?