Timothy C. Lethbridge

Department of Computer Science

University of Ottawa

Ottawa, Ontario, Canada K1N 6N5

(613) 564-8155 tcl@csi.uottawa.ca

When surveying use of our lab’s knowledge acquisition (KA) system, CODE (Skuce et al, 1989, Skuce 1991a), over the past few years, one major point in particular strikes us: CODE’s use as a creative assistant has often been more valuable than the knowledge bases we have built/\..

For example, during a major industrial application of CODE to software requirements analysis, it took us many months to establish a substantial knowledge base and to convince project team members to use it as the central conceptual repository and documentation source (Skuce 1991b). Almost from the beginning of our involvement however, team members were able to benefit from the process of building the knowledge base: By the manipulation of conceptual structures through an effective user interface, complex issues with which they had previously struggled were resolved.

We have taken to creating very small knowledge bases with CODE to develop ideas in our research (including for this paper). The knowledge bases frequently become obsolete as ideas rapidly evolve; but that does not matter: the value has come from use of a KA system as an analysis tool to get us over the creativity ‘hump’. In one domain, the design of our core knowledge representation, we have created knowledge bases from scratch three separate times in order to make use of the creative potential of the process.

The focus of this paper is the problem of how to optimize the creative potential of KA systems, whether the resultant knowledge base is to be maintained or not. We start by analysing the creative aspects of KA so we can understand what needs optimizing. Then we develop a hypothetical model to describe the process a user goes through when using a KA system creatively. Finally we use an example user session to make some suggestions about creative KA. These suggestions affect 1) knowledge representation, 2) ontology and 3) user interface.

The general meaning we give to ‘knowledge acquisition’ (KA) is any process leading to the addition or refinement of knowledge (i.e. concepts) in a knowledge base. We prefer this broader meaning to that restricted primarily to ‘expert systems’ or ‘expertise’ (Gaines 1989); we do not however consider a task as common as writing a document to be KA, unless it is part of a process leading to knowledge base building.

By creative knowledge acquisition we mean KA of primarily new knowledge, due to direct manipulation of and feedback from conceptual structures evolving in parallel in minds and in a knowledge base. It is a largely individual process, comparable to creative writing, but with much more potential for tool support than possible with natural language composition.

By new knowledge, we mean knowledge not present in the mind of the domain expert or in the knowledge base before the KA session (although less refined knowledge may have been present). New knowledge is originated (i.e. created) in mind or machine using processes (deductive, inductive, intuitive etc.) that detect distinctions or patterns in pre-existing knowledge. Processes that are not considered to be originating knowledge include observing, translating, loading, decompressing, inheriting and combining (as long as these processes lead to nothing distinctive being observed about the knowledge).

We consider knowledge to be composed of concepts which are mental or mechanical representations of anything that can be ‘thought’ about. Two important types of concepts are independent concepts and the relations between them that we call property occurrences.

The early stages of creative KA can typically be described as ‘individual brainstorming into a knowledge base’, although brainstorming has a connotation of haphazardness that should not be applied to all creative KA.

Two particular aspects of KA-related literature are relevant to creative KA. One is the study of serendipity, the discovery of knowledge one is not looking for (Roberts, 1989). Creative KA involves more than just enhancing chances for serendipity: One often is looking for specific knowledge one does not possess. The other useful aspect of the literature is the use of KA in design (Reich, 1990; Mark, 1990); designers have much to benefit from the enhancement of creative potential.

Before we describe our process model for creative KA, we present a conceptual analysis comparing creative KA to general KA. The analysis is based on classifying KA tasks with the objective of identifying where we might target technique improvements. Other KA classification literature tends to be organized by subtask, technique, tool, application area, assessment criterion etc. (e.g. Wetter and Woodward 1990)

Our analysis is in the following ten subsections, each describing a semi-independent property on whose scale we can place any KA task. Following each property we indicate in what way creative KA tends to be specialized. Our model, described later, is developed with these specializations in mind, but may be applicable in more general KA situations.

The properties are listed in order, such that those that most distinguish creative KA are placed first. The properties are phrased as questions to be posed about each task; under each question is a list of points outlining a rough scale in which the answer should fall. The points listed for each property are not meant to be the only possibilities, nor are they meant to be mutually exclusive or necessarily form a logical progression. Within a given KA task, a property typically involves a range of points on its scale, which may apply at once or at different times; this implies that some aspects of a task may be considered creative and some may not.

Answer d most strongly characterizes creative KA, although answer b also frequently applies. The knowledge is already ‘created’ in form a, and form c requires emphasis on elicitation techniques that detract from spontaneous creativity (see property 4 below).

Creative KA falls at the latter end of this spectrum; creation of the knowledge is done non-algorithmically by human thought processes (frequently intuitive, and generally poorly understood by today’s science).

Creative KA follows the pattern of point c. The idea is that feedback from the interactive process stimulates creativity.

Although any machine assisted technique can assist creative KA, technique g, where the user is most in control, best characterizes our vision of creative KA.

There is extensive discussion of elicitation techniques in the literature. A decomposition comparable to the above may be found in (Gaines, 1990, p 9-19). A good survey of those applicable to expert systems is (Neale, 1989). Interpretive structural modelling is discussed in (Warfield, 1976).

Creative KA leans towards methods f and g, although all of c through g are possible. In one project we had experiences with methods b through f, and our system demonstrated its creativity-stimulating potential to the greatest extent using method d. We believe, however that this was an artifact of our having much greater tool expertise than the domain experts did when they used methods e and f. A major thrust of our research is to increase the usability of creative KA systems in order to make methods f and g the norm.

We have used all methods except b, and although method a tends to stimulate the most creativity, little refinement of the knowledge is possible. Our concept of creative knowledge acquisition involves only the individual-work phases of methods b to e.

Creative KA can have any of these purposes, but those later in the list are more typical: Creative knowledge is gathered more for immediate problem-solving than with the delayed objective of solving problems with the knowledge. The process of representing knowledge tends to be more important than the knowledge. The addition to knowledge in people’s minds tends to be more important than the addition of knowledge to the KB.

Typically creative KA is performed on small discrete 'problems' (property 7, above) and created by individuals (property 6) hence knowledge base size is naturally limited. KB sizes have been limited by technology, but many people believe in a ‘bigger (within limits) is better’ philosophy, with long-term projects that attempt to capture much of the detail in a domain. While we laud such efforts, we believe in the need to develop special technologies for ‘personal KB’s. For a somewhat imperfect analogue, consider the differences between typical use of a large database system and typical use of a spreadsheet program.

Complexity measures such things as interconnectedness, non-linearity, dimensionality, degree of meta-representation, degree of projection etc.

While smaller in scope, we see creative KA as tending to deal with greater knowledge complexity than mainstream KA. Complex problems are what people need creative KA for. Only in small KA efforts can time be afforded to expand concepts to extreme levels of detail.

While other types of serious KA require someone who truly knows the concepts (or has recorded them in documents), creative KA may involve non-expert knowledge source persons. Such persons may have peripheral knowledge and be using KA to expand their expertise (e.g. developing theories). Alternatively, such persons might be using KA to formulate ‘naive’ suggestions to real experts (in the same manner that someone might design their own house).

In this section we describe our hypothetical model of the creative KA process. The model has two related components: 1) a model of knowledge-base editing objectives that may shed some light on how to assist a user in sequencing a creative KA task, and 2) a model of informal knowledge that can help explain how to prevent the user from getting bogged down too early in details.

We have developed the model in order to explain observed phenomena and to provide a framework for ideas about the improvement of creative KA. We make no claims about the cognitive correctness of our model; others may wish to persue such research. For us the model is primarily an aid to thought that we have found useful. In section 4 we will present several recommendations, several of which arise from the model.

Our model is not a ‘user model’ in the standard sense of the term. There is a literature on ‘user models’ and ‘information source models’ (e.g. Thost, 1989). We primarily differ in that we are not dealing with ‘machine conceptualizations of particular users’; we do not emphasize automatically adapting interactions to such models. Instead we have a single general model that we use 1) to structure our own analysis, and 2) to suggest possibilities for facilities the user could call on, under his or her control, to aid memory and planning of a creative KA task.

We model the mental state of a creative KA system’s user as a loose collection of concept-elucidation objectives. Concept elucidation can be thought of as clarifying a concept, expanding the level of knowledge associated with it, building a conceptual substructure centred around it, or ‘formalizing’ it (see next subsection). Experience leads us to believe that creative productivity can be enhanced if the user arranges the objectives in a hierarchy.

We consider an objective to be a mental entity; the process of its achievement is performed simultaneously in mind and KA system. On the KA system side, the user builds machine conceptual structures; in an ideal system these correspond to the mental conceptual structures being built at the same time. We envisage the machine representation process assisting the parallel mental process — maintaining the network of complex details of which the mind would otherwise lose track. In other words, we see a sort of mind-machine symbiosis.

The hypothesized collection of objectives develops in steps as follows: The user mentally searches for, and seizes on, an idea for a conceptual structure that, if represented, would help achieve a particular elucidation objective. Representing the idea becomes a sub-objective. The sub-objective, and possibly parent objectives, are removed if the representation of the idea’s central concept satisfies them, i.e. if the user feels that no further elucidation is necessary, that the conceptual structure has been completely built.

Steps of the preceding form take place iteratively until there are no more objectives to achieve. The objective collection grows and shrinks, and the conceptual structures in mind and machine become elucidated.

At each step, just one concept is actively changed in mind and machine (although passive consequences caused by effects such as inheritance usually also occur). It is important not to confuse the objectives with the conceptual structure being built, even though in our model each step involves one objective and one central concept.

To summarize the above, at each step:

We do not intend by our idea of a loose ‘objective hierarchy’ to suggest that the minds of creative KA system users work in a Prolog-like manner. To avoid this perception, it has been suggested that a blackboard architecture might be a superior model. We agree that such an approach could be useful, but it ignores the creative advantage we ascribe to structuring creative thinking hierarchically. Further research may shed some light on whether our view is justified.

We describe the process of elucidating a concept in the machine as making it more formal (Lethbridge, 1991a). We use the word ‘formal’ with its general meaning of ‘having shape or form’. A concept becomes more formal if more links are made from it to other concepts and/or if other concepts it is linked to become more formal. Two concepts are linked if a property occurrence of one refers to the other (directly or indirectly).

In our model, the user’s decision that an objective is achieved is based on his or her perception that the objective’s central concept has reached a desired level of formality. The desired level of formality should presumably be higher for concepts of a higher-level objective; in fact if a user elucidates peripheral concepts to an excessive level of formality, he or she can be said to be digressing (helping to prevent this is a separate research problem).

When many concepts are first represented in the machine, they are informal since they lack links; they typically contain only descriptive text. We envisage the early phases of creative KA as involving the addition of large numbers of informal concepts very quickly (one step every few seconds). In later phases we see greater emphasis on increasing formality as objectives become more nearly achieved. In many applications, we see KA eventually moving out of a creative phase into one of validation and debugging — at such a time we envisage use of a somewhat different set of KA tool facilities from those used to enhance creativity. We also envisage a KA task moving from creative mode to validation mode and back again, several times.

There are three primary aspects of the creative KA process where our model gives rise to ideas for machine KA assistance: 1) Helping the user generate the initial idea that forms an objective, 2) Allowing the user to record his or her stream of consciousness without impedance, 3) Helping the user to organize his or her work:

In section 4 we will outline some recommendations that arise from these points, but first we present a slightly more detailed look at the third point.

We can attempt to keep a machine model of the user’s elucidation objectives by maintaining a history of the corresponding concepts the user edits. By comparing the relationships of concepts in this history we can arrange it in a hierarchy ordered by sub-objective. Various aspects of the user interface can manipulate this hierarchy to assist the user:

Although we have not yet applied these techniques to knowledge base editing, preliminary experience with some of these ideas in a CASE environment (an enhanced Smalltalk browser) has convinced us of their usefulness.

Although we described the above model using the perspective of a knowledge source person working in intimate comunion with a creative KA system, we do not discount other possibilities. For example there could be a knowledge engineer involved (property 5), or there could be several knowledge sources (property 6). Such possibilities do not invalidate our model, they merely complicate it. For example, the objective hierarchy would be the abstract ‘consensus’ objective hierarchy (the idea of an objective hierarchy is abstract anyway, so making it more so is not of great concern!)

In this section we step through an example creative KA session, making suggestions about how to prevent various creativity problems. Figure 2 (which is split into several parts) shows the session’s progress.

We assume the presence of a basic knowledge representation (KR) system with a frame-like knowledge structure and multiple inheritance. Our suggestions describe facilities needed above and beyond this. Some of our suggestions are well-known, others less so.

The suggestions relate to three areas of KA system requirements: 1) knowledge representation, 2) ontology and 3) user interface. Although these tend to be researched separately, we believe it is important to consider them together for the purpose of solving problems such as creativity enhancement.

The list of recommendations is not intended to be comprehensive. In particular, it is not at all concerned with the following problems we believe are orthogonal to enhancing creativity.

Some of our recommendations may help solve these problems, but some might in fact hinder such solution. For example, we discourage the user from stopping to worry about the consequences of entering a piece of knowledge in the early stages of KA. Such stopping may help get the knowledge right and ensure it follows standards, but it can hinder creativity.

We have made the observations described in this section repeatedly in a number of small KA projects. The particular example is synthetic and extremely over-simplified, but the domain is that of a real project.

Our research work is ongoing, so we expect to further refine the ideas in the future. Some of the recommendations use the terminology and conceptual framework of CODE, but we believe they can readily be transferred to the context of any other system. For greater detail on some issues see (Lethbridge, 1991b). For most of the recommendations, there exist compliant KA systems; we believe however, that no system available today complies with all recommendations.

Imagine the following: A designer has in mind a new type of telephone service. He or she mentally develops the idea, but then realizes it has many complexities that are hard to organize in natural language, diagrams or other conventional media. He or she also wants to be able to explore options and receive automatic feedback about the idea. For these and other reasons it is decided to use a ‘creative KA’ tool.

The user starts with a top-level objective

The user has the lower-level objective to name the concept (despite suggestion a3 above, naming in this case is easy). Once naming is done, the naming objective is removed from the objective hierarchy. Naming is but one of a vast number of ‘potential objectives’ opened up by the top level objective, naming is merely the first that occurred to the user.

It has occurred to the user that there is a microprocessor involved somehow. The first step is to record a new concept.

The user has not finished specifying knowledge about the microprocessor, but his or her stream of consciousness is diverted into recording the fact that the telephone is to have a program. This is a sub-objective of the main objective.

As a further step towards specifying the microprocessor, the user indicates that NNCF telephones have electronic parts.

Knowledge acquisition systems can be used in a largely creative mode where the actual knowledge represented in the machine may be of secondary importance to the inspirations created in the mind of the system’s user. By analysing what contributes to creativity when performing KA, we have identified several key recommendations to enhance the creative potential of a KA system.

Our production KA system, CODE Version 2.4, follows some of the recommendations, and gave us many insights leading to the development of the other recommendations. At the time of writing we are implementing the latest version of CODE, version 4, which we anticipate will eventually fulfil all the recommendations.

A general recommendation for a creative KA system can be summarized as follows: "Facilitate the user’s thinking - avoid inhibiting it". To be more precise, we present a list of recommendations below. Following each recommendation we indicate:

Support for this research is or has-been provided by Bell-Northern Research and the Natural Sciences and Engineering Research Council of Canada. The author thanks Douglas Skuce, Ingrid Meyer, Yves Beauvillé and Bruce Porter for their ideas and inspiration.