**14. Curiosity, inspiration, learning and understanding – narratives**

Turning to the future now, the promise model has some other nice features. Often, we claim to have understood something when we are happy to stop looking for further explanation. This usually happens when we are able to construct a satisfactory story or explanation about it. These stories or explanations stop in often arbitrary places – they are more about sating our curiosity to some level of satisfaction than total revelation. Humans evolved language 26 Will-be-set-by-IN-TECH

no simpler. The subjectivity of this intrinsic cost might also explain why some people find it

The economic perspective we are pursuing here suggests a simple strategy for reducing personal cost by an end user that has to do with short-circuiting others' predefined or

**Hypothesis 6** (Personal simplification strategy)**.** *Each individual student or recipient of knowledge begins by remapping apparent categories of information used by the source into a personal reduced set of trusted categories, according to their own world view and experience. In this way the cost of lookup,*

In modelling terms, we can imagine forming usage-categories called, say, 'virtual bundles of knowledge promises', i.e. virtual roles for the things a user promises to accept, which any

More work will be needed to identify what the optimum approach might be in certain circumstances, and this could depend on a number of factors, so I shall leave the subject

If an ontology is not determined by a standardizing authority, how can we be certain that anyone will end up understanding each other? The story of the Tower of Babel comes to mind, as one advocates tearing down the standard ontologies. In fact, I believe that the underpinning of knowledge by these spanning trees is entirely unnecessary. It is rather up to each and every

What the promise model underlines is that every agent individually promises only its own intended meaning, and in fact no two agents can truly know if they mean the same thing. Rather than seeing this as a problem to be forced into submission, it is better to accept this as the nature of reality and deal with the uncertainty. Only an independent third party can determine whether or not they *seem* to agree for all intents and purposes. The frequency of use will determine how stable word usage is. Note that the irregular verbs are those that are most frequently used. Less well used words tend to be normalized into common patterns quickly

The main difference in the emergent approach is the distribution of cost. For the authoritative ontology, the up-front cost of contribution and usage is high, and it assumes expert knowledge. For the emergent context approach, there is no initial cost, but rather one must promise to practice over time to retain meaning. The advantage of a purely linguistic classification is that it is not a separate rehearsal from daily usage. We have little choice but to practice language, so in some ways the overhead is gratis, or at least can be 'charged to a

Turning to the future now, the promise model has some other nice features. Often, we claim to have understood something when we are happy to stop looking for further explanation. This usually happens when we are able to construct a satisfactory story or explanation about it. These stories or explanations stop in often arbitrary places – they are more about sating our curiosity to some level of satisfaction than total revelation. Humans evolved language

**14. Curiosity, inspiration, learning and understanding – narratives**

harder than others to learn or accept knowledge from certain sources than others.

authoritative categories by recategorizing everything in his or her own set.

knowledge agent is free to edit and manipulate as it sees fit.

dangling on this point, as an opportunity for future work.

**13. How can we be certain about meaning?**

user to apply such a tree as a filter if they so desire.

*mistrust and unfamiliarity is reduced.*

to reduce the cost of recall.

different account'.

through story telling. It is entirely possible that our brains are wired to support this form of narrative.

Consider what might happen if we looked up Einstein's famous equation *E* = *mc*<sup>2</sup> in a knowledge base. This simple looking equation is associated with a huge amount of popular culture about Einstein, and most people would immediately think of him, but there is nothing unique or 'copyrighted' about it. A nutrition expert who had never read any physics might use this formula for something quite different, such as "Eating is munching and chewing twice".

Still, one context dominates above all others and that is physics. Under this context, there are many associated ideas. When we think of *E* = *mc*2, we might associate it with the atomic bomb, or nuclear power, or mass-energy conversion, or with a funny photograph of Einstein pulling tongues at the camera.

In a book one could integrate all of these apparently unrelated meanings from cover to cover, weaving them into a story, with a progressing storyline that explains, organizes and blazes a repeatable trail for all these ideas, or one could go directly to look up keywords in an index. The table of contents in a book spans the highlights of the story, whereas the index is a quite different covering of the subjects that pays little attention to the ordering or developments in narrative, or the totality of the theme in the book.

The concept of a story is large in human culture, but as far as I can tell very little attention has been given to them in Knowledge Management research. In work with Alva Couch, I have tried to remedy this by exploring a simple notion of stories, including the notion of automated storytelling, by identifying causal associations between topics, see Burgess (2009); Couch & Burgess (2009; 2010). A table of contents, in a book, is a rough outline of the story told by the book at a very high level. It gives a different perspective on a book's content than the index does (yet the index is what current KM technology is almost exclusively focused on). Knowledge technology needs to support the idea of storylines, in which ideas and information build upon the context of earlier information, because this is how humans communicate, see Wolf (2007).

#### **Definition 20** (Story)**.** *A collection of topics connected together by associations in a causative thread.*

Causality (i.e. cause and effect) can be embodied in associative relationships such as 'affects' or 'always happens before', 'is a part of' etc. These relationships have a transitivity that most promised associations do not have, and this property allows a kind of automated reasoning that is not possible with arbitrary associations.

Automated story generation has been discussed by myself and Alva Couch in , see Couch & Burgess (2009; 2010), so I will not repeat the detailed arguments here. Today, there are no semantic knowledge models that are able to model creative narratives by association, or even ordered tables of contents in books for that matter! This is an extraordinary omission and a key capability in integrating random access knowledge with documents.

It is worth studying this possibility to derive new and 'unknown' stories from emergent repositories of knowledge promises. In this way, one could imagine discovering a new story about *E* = *mc*<sup>2</sup> that has never been told before, derived perhaps from the contributions of a swarm of twenty different individuals who were not even thinking about this matter.

Understanding more about the principles of story detection could also have more far-reaching consequences for knowledge than just automated reasoning. In school, not all students find it easy to make their own stories from bare facts, and this could be why some students do better than others in their understanding. We tend to feel we understand something when we can

And the Emergence of Ontology 29

What's Wrong with Knowledge Management? And the Emergence of Ontology 177

The tension between the desire to hierarchically divide and conquer subjects and the freedom to develop storylines unconstrained is likely to haunt knowledge management for many years to come. In the writing of this essay, for instance, I have striven to seek a balance between serving two masters: to organize things into a simple hierarchy of sections (for later 'dipping into the story' i.e. for reference), while at the same time recognizing that the whole narrative much be readable from start to finish. It is through the storyline that the illusion of understanding is most likely to emerge, because there we control the context of information from moment to moment. A novel never has to satisfy the former constraint, and is therefore

The likelihood that we will ever unify meaning into a single, standard, crystalline tree of concepts is about the same as the likelihood of unifying all the world's cultures into one. The evidence from social networking, see Newman (2003); Watts (1999), suggests that the human desire for social interaction evens out and normalizes: like swarming behaviour, we follow involuntarily the influences of others, and this leads to a condensation that has manageable

The final answers about knowledge management lie probably with social anthropology. It will be a challenge for more empirical studies to come up with evidence for the success or failure of the suggestions contained here. In the mean time, there seems to be little to lose by trying a promise approach, so I leave it to readers to explore these simple guidelines in practice.

Albert, R. & Barabási, A. (2002). Statistical mechanics of complex networks, *Reviews of Modern*

Bergstra, J. & Burgess, M. (1994-now). *An engineering approach to promises*, work in progress. Bergstra, J. & Burgess, M. (2006). Local and global trust based on the concept of promises,

Bonabeau, E., Dorigo, M. & Theraulaz, G. (1999). *Swarm Intelligence: From Natural to Artificial*

Burgess, M. (2004). *Analytical Network and System Administration — Managing Human-Computer*

Burgess, M. (2005). An approach to understanding policy based on autonomy and voluntary

Burgess, M. (2009). Knowledge management and promises, *Lecture Notes on Computer Science*

Burgess, M. & Fagernes, S. (2007a). Laws of systemic organization and collective behaviour

Burgess, M. & Fagernes, S. (2007b). Norms and swarms, *Lecture Notes on Computer Science* 4543

Burgess, M. & Fagernes, S. (n.d.). Voluntary economic cooperation in policy based management, *IEEE Transactions on Network and Service Management* p. (submitted).

cooperation, *IFIP/IEEE 16th international workshop on distributed systems operations and*

in ensembles, *Proceedings of MACE 2007*, Vol. 6 of *Multicon Lecture Notes*, Multicon

(Proceedings of the first International Conference on Autonomous Infrastructure and

Barabási, A. (2002). *Linked*, (Perseus, Cambridge, Massachusetts).

*Systems*, Oxford University Press, Oxford.

*management (DSOM), in LNCS 3775*, pp. 97–108.

*Systems*, J. Wiley & Sons, Chichester.

• The role of repeated interaction in cementing normative language.

• The economics in the dynamics of agreement.

a purer form of writing.

proportions.

**16. References**

*Physics* 74: 47.

5637: 95–107.

Verlag.

*http://arxiv.org/abs/0912.4637* .

Security (AIMS)): 107–118.

tell a convincing story about it. With more formal principles behind an effort to understand stories, technology could help struggling students to grasp connections better, and one could imagine a training program to help basic literacy skills.

We don't have time to explore all these details in this essay, except to point to this as a promising (pun intended) area of research for the future. I think that storylines combined with a linguistic approach to ontology can go a long way to addressing the deficiencies of today's web ontology methods.
