**3. The shortcomings of databases and search**

Stockpiling information does not increase knowledge in any real sense, but this is what we do in libraries and databases. According to the IT Infrastructure Library's knowledge management guide, see of Government Commerce (2000), the knowledge ladder goes through four phases: DIKW, or from mere Data (numbers, texts, assertions, etc), to Information that has some context around it, which in turn becomes knowledge when we are able to apply it and use it as a tool. Then finally there is Wisdom, down the line as something we aspire to.

The ability to search within such a repository can throw up all kinds of possibilities, but usually either too few or too many to make real sense of. With the advent of Google's PageRank, we have come to trust statistical 'mass-voting' as a guide to relevance. Popular voting a normative process in which common habits converge and encourage the spread of the habit to others. What is interesting about the enormous success of Google is that is does not try to be too sophisticated. It basically offers up some sorted data to users, who must then use their brains and exploring skills to rummage through he results. I believe we should learn something from this.

Databases store data row upon row, like a warehouse. They form the bread and butter technologies for storing data in a retrievable way, by associating names and addresses with information, so that a catalogue of entries can be made and searched by users. The limitation of most databases is that they have fixed tabular structures or schemas into which all information must fit. They are just 'data' - void of interpretation, and inaccessible to non-specialists. This makes sense from the viewpoint of machine retrieval, but it puts a burden of creativity on the user to standardize something that it perhaps only loosely understood. As a result, tidy database models tend to bloat into garbage dumps for all kinds of semi-intended annotation.

I shall argue that this is part of a common flaw in approaches to Knowledge Management: the first flaw of KM is that the user is expected to subsidize the technology in order to make it work. The tools of knowledge management, far from helping, often put up barriers and costs for the user. It is interesting to see the great leaps and bounds that have been made in user interface design, and compare this to the poverty of progress in knowledge management.

A second problem with database search is the attempt to use formal logic to whittle away at a large number of possible matches, see Strassner (2007). Even today's experimental derivative technologies for semantic search try to use assumed facts to reason in first order logic, hoping to find enlightenment. The problem with logic is that is needs strictly defined, inflexible categories to work with – and this is the second flaw of knowledge modelling. As one whittles away results, the result tends to be either utterly obvious, or empty due to over constraint.

### **4. Knowledge representations**

2 Will-be-set-by-IN-TECH

Concepts like 'taxonomy' and 'ontology' abound, both of which we must return to in this essay, and methods are borrowed from computer logic to try to reason about categories of

The state of the art in knowledge management today, may be summarized by these things, in

The bulk of information today is available on 'the Web'. Although the web is only a publishing mechanism, it has played a genuine role in encouraging the documentation and dissemination of knowledge, by making it easy (cheap) for people to participate in a shared process. Wikis have helped to make this happen (with flagship examples like Wikepedia), but the ways in which Wikis are used, in a wider context, suggests that they have a less than effective track-record as long-term repositories of knowledge (see below for some speculations on this). Today, we aspire to greater sophistication. Semantic technologies attempt to go beyond the pure text-search approach to using the web, by attaching actual intended meaning to words. Tools like RDF, see W3C (n.d.), Topic Maps, see Moore (2001); Pepper (2009), OIL, OWL, Protegé, see Strassner (2007), etc., attempt to use intended meaning to provide better hints and suggestions to knowledge-seekers by wrapping information in 'meta-data' – or information

Semantic webs or networks (often called ontologies today) begin with the desire to classify topics 'meaningfully'. One reason is to be able to disambiguate different patterns of usage as we talk about things. An ontology is a model of someone's particular 'world view'. Technically, an ontology is defined as 'a set of specialized concepts within a domain' (from the Greek logos (talk about) and ontos (that which exists)). It is the term used for describing

Judging both from my own personal experience and from existing research, ontologies are notoriously hard to create for a number of reasons, see Dicheva & Dichev (2005); Moench et al. (2003). They typically represent domain expertise, they need to be populated with knowledge by domain experts; however, the technologies for doing so are not user-friendly and so these trained experts need other experts (trained knowledge engineers) to make the models on their behalf – experts, requiring more experts. All this places a high cost on creating useful systems, and it has essentially killed most efforts to use semantic technologies, by trying to be too clever. This is one of the issues I would like to address here. The other main problem with technologies today is that they have little capacity to teach users about new things they didn't already know of. So we would like to find a way to offer new insights to users, as they interact

Stockpiling information does not increase knowledge in any real sense, but this is what we do in libraries and databases. According to the IT Infrastructure Library's knowledge management guide, see of Government Commerce (2000), the knowledge ladder goes through four phases: DIKW, or from mere Data (numbers, texts, assertions, etc), to Information that has some context around it, which in turn becomes knowledge when we are able to apply it and use it as a tool. Then finally there is Wisdom, down the line as something we aspire to.

information, see et al. (2003); Strassner (2007).

• Ontology or semantic modelling as a dream of something better.

for computers to use in cataloguing and reasoning to capture domain expertise.

• The Web, and its search engines. • Wikis for collaborative writing.

my view: • Databases.

domain knowledge.

with a knowledge base.

**3. The shortcomings of databases and search**

Knowledge is what you get when you combine context and experience with information so that its consumers can form their own mental model, believe it and apply it for themselves. The way we represent this mixture can vary enormously. Examples range from the obvious

to the subtle, and as scientists we should also have the humility to recognize art as a form of semantic commentary with a definite role to play in communicating things with cultural context.

**Definition 1** (Knowledge Representation)**.** *A piece of work that attempts to communicate observations about something, e.g. experiences, opinions, or some other form of human understanding.*

Examples of Knowledge Representation include the obvious books, articles, stories. We also have keywords, titles, tables of contents, index entries, pointers, references and relationships to other works. Paintings, songs, music, and all other forms of communicable works may be thought of as different representations for knowledge.

How do we know when something is knowledge? This brings us to the third flaw with Knowledge Management. It is generally assumed in the field of Knowledge Management that facts and knowledge are authoritative, i.e. that which is knowledge is defined for the

And the Emergence of Ontology 5

What's Wrong with Knowledge Management? And the Emergence of Ontology 153

To motivate a solution to the three problems above, I want to take a simple point of view based on a modelling framework known as *promise theory*1, which has yielded some success in understanding issues related to knowledge management: organization and cooperation. Promise theory is a way of describing how individual 'agents' advertise their behaviour so that they can form expectations of one another. They do this by communicating promises. An agent can be a human, group of humans, or some other entity being steered by humans. Like all models, it is intentionally simplistic, but I shall claim (in the interests of knowledge) that this approximation can be a good anchor point from which to begin a more sophisticated

Promises are about trying to understand and govern expectations, given that we (i.e. people or agents) have at best incomplete information about the world. If we knew everything, there would be no use for promises. Promises made by agents to each other act as a kind of signalling mechanism to raise or lower expectations. Promise theory is uniquely suited to studying knowledge management because it captures something about the human condition:

**Definition 3** (Promise)**.** *The public expression of something that is, has been, or might be intended, made by an individual agent (the promiser), to a limited audience called the scope of the promise, which*

A *promise*, in this technical meaning, is a statement of intent, by an agent, that is meant to reinforce another's expectation that the intention will turn out to be true. For instance:

1. I promise to feed your cat – expectations about what might happen in the future and cannot

2. I promise I have fed your cat – expectations about what might have happened in the past

3. Feed cats seems a promising strategy – we speak of the expectation that feeding cats will

Clearly each of these statements can be considered a matter of knowledge. In each case, promises are about expectations2. Consider next the following promises about someone's

• I promise that a 'chapter' means a sub-division of a religious order, in the context of

<sup>1</sup> Actually this phrase was coined rather loosely, and its authors have tried to find a more specific name for it such as 'micro-promises' that is less omnipotent in its claims, but simplicity often reigns above

<sup>2</sup> In philosophy, promises are usually thought of as a moral issue, but here we shall discard any moral connotations and deal exclusively with expectations. The promise of good weather, for instance, means that there is some expectation on the part of listeners that the weather will turn out well. This is not a promise made by morally good or evil clouds, but rather an imagined intention (embodying a harmless

• I promise that a 'chapter' means a part of a book, in the context of literature.

• I promise that chapters consist of words, in the context of literature.

anthropomorphism) made by holiday-makers and wedding planners, etc.

subjective individuals working together in a partially cooperative environment.

*generally includes the promisee(s), i.e. the intended recipient(s) of the promise.*

**5. Promises as a model for knowledge**

understanding.

**5.1 The promise model**

yet be confirmed.

but has not yet been verified.

understanding of certain terms:

reason, as we shall see in this essay.

churches.

bring positive outcomes in the future.

masses by schools and universities or experts, or other figure-heads. This however is patently false. Accepting something as knowledge is an entirely voluntary choice that every one of us makes at our discretion. No one can force us to believe or accept what is represented. We must therefore adopt a model of knowledge based on voluntary adoption if we hope to understand something significant.

**Definition 2** (The three flaws of knowledge engineering)**.** *Current approaches to knowledge make three errors:*


#### **4.1 Topic maps and RDF**

One of the representations of knowledge index that has been developed over the years is the Topic Map, see Pepper (2009). Topic Maps are a form of subject index with detailed annotations that explain the relevance of associations. Topic maps have been made into a standard for the representation and interchange of knowledge. The ISO standard is formally known as ISO/IEC 13250:2003.

Topic Maps have several competitors in this space, the Resource Description Framework (RDF) being the best known, see W3C (n.d.). What makes Topic Maps interesting is that they were designed for human consumption, where the emphasis for RDF is on artificial machine reasoning.

A topic map represents information using an index model called TAO:


Every knowledge item we want to talk about is a topic which has a name and a type-category. Relationships to related issues are made by association (e.g. see also ...). Finally, occurrences are pointers to specific documents or other representations of knowledge that are asserted to be relevant to the named topic.

As I studied Knowledge Management in the beginning, I was drawn to topic maps over RDF and OWL, as it seemed to avoid wallowing in syntax and logic. Even Topic Maps have sought validation through this kind of formal computer science approach however, and I believe that the biggest flaw in Topic Maps was in choosing to model in the classic database approach. This led to unnecessary constraints, such as non-overlapping type categories, see Dietz (2006); Kipp (2003); W3C (n.d.).

The reason this approach has failed is a basic limitation on human willingness to get involved in highly technical reasoning. Modelling knowledge with logic is very hard, and not very convincing as knowledge is based on human faculties which are seldom fully rational and are never uniquely structured. On a scale of posting a note on the refrigerator to publishing a scientific paper in Nature, Topic Maps and RDF are much closer to the latter. That is simply too hard for most users, and hence these technologies curse Knowledge Management to be the domain of wizards and kings, unable to capture the knowledge of everyman.

### **5. Promises as a model for knowledge**

To motivate a solution to the three problems above, I want to take a simple point of view based on a modelling framework known as *promise theory*1, which has yielded some success in understanding issues related to knowledge management: organization and cooperation. Promise theory is a way of describing how individual 'agents' advertise their behaviour so that they can form expectations of one another. They do this by communicating promises. An agent can be a human, group of humans, or some other entity being steered by humans. Like all models, it is intentionally simplistic, but I shall claim (in the interests of knowledge) that this approximation can be a good anchor point from which to begin a more sophisticated understanding.

#### **5.1 The promise model**

4 Will-be-set-by-IN-TECH

masses by schools and universities or experts, or other figure-heads. This however is patently false. Accepting something as knowledge is an entirely voluntary choice that every one of us makes at our discretion. No one can force us to believe or accept what is represented. We must therefore adopt a model of knowledge based on voluntary adoption if we hope to understand

**Definition 2** (The three flaws of knowledge engineering)**.** *Current approaches to knowledge make*

*3. Knowledge categories are defined authoritatively, but users only accept them voluntarily, if the*

One of the representations of knowledge index that has been developed over the years is the Topic Map, see Pepper (2009). Topic Maps are a form of subject index with detailed annotations that explain the relevance of associations. Topic maps have been made into a standard for the representation and interchange of knowledge. The ISO standard is formally

Topic Maps have several competitors in this space, the Resource Description Framework (RDF) being the best known, see W3C (n.d.). What makes Topic Maps interesting is that they were designed for human consumption, where the emphasis for RDF is on artificial machine

Every knowledge item we want to talk about is a topic which has a name and a type-category. Relationships to related issues are made by association (e.g. see also ...). Finally, occurrences are pointers to specific documents or other representations of knowledge that are asserted to

As I studied Knowledge Management in the beginning, I was drawn to topic maps over RDF and OWL, as it seemed to avoid wallowing in syntax and logic. Even Topic Maps have sought validation through this kind of formal computer science approach however, and I believe that the biggest flaw in Topic Maps was in choosing to model in the classic database approach. This led to unnecessary constraints, such as non-overlapping type categories, see Dietz (2006);

The reason this approach has failed is a basic limitation on human willingness to get involved in highly technical reasoning. Modelling knowledge with logic is very hard, and not very convincing as knowledge is based on human faculties which are seldom fully rational and are never uniquely structured. On a scale of posting a note on the refrigerator to publishing a scientific paper in Nature, Topic Maps and RDF are much closer to the latter. That is simply too hard for most users, and hence these technologies curse Knowledge Management to be

the domain of wizards and kings, unable to capture the knowledge of everyman.

*1. Users are expected to work too hard to interact with knowledge.*

A topic map represents information using an index model called TAO:

*2. Knowledge is treated as a logical framework.*

*context of their own experience.*

**4.1 Topic maps and RDF**

known as ISO/IEC 13250:2003.

be relevant to the named topic.

Kipp (2003); W3C (n.d.).

• Topics, or subject fragments (atoms).

• Associations that explain relevance and meaning. • Occurrences of independent information about topics.

something significant.

*three errors:*

reasoning.

Promises are about trying to understand and govern expectations, given that we (i.e. people or agents) have at best incomplete information about the world. If we knew everything, there would be no use for promises. Promises made by agents to each other act as a kind of signalling mechanism to raise or lower expectations. Promise theory is uniquely suited to studying knowledge management because it captures something about the human condition: subjective individuals working together in a partially cooperative environment.

**Definition 3** (Promise)**.** *The public expression of something that is, has been, or might be intended, made by an individual agent (the promiser), to a limited audience called the scope of the promise, which generally includes the promisee(s), i.e. the intended recipient(s) of the promise.*

A *promise*, in this technical meaning, is a statement of intent, by an agent, that is meant to reinforce another's expectation that the intention will turn out to be true. For instance:


Clearly each of these statements can be considered a matter of knowledge. In each case, promises are about expectations2. Consider next the following promises about someone's understanding of certain terms:


<sup>1</sup> Actually this phrase was coined rather loosely, and its authors have tried to find a more specific name for it such as 'micro-promises' that is less omnipotent in its claims, but simplicity often reigns above reason, as we shall see in this essay.

<sup>2</sup> In philosophy, promises are usually thought of as a moral issue, but here we shall discard any moral connotations and deal exclusively with expectations. The promise of good weather, for instance, means that there is some expectation on the part of listeners that the weather will turn out well. This is not a promise made by morally good or evil clouds, but rather an imagined intention (embodying a harmless anthropomorphism) made by holiday-makers and wedding planners, etc.

And the Emergence of Ontology 7

What's Wrong with Knowledge Management? And the Emergence of Ontology 155

promise-agents, in the first case. They will make certain promises to one another, which will involve agreeing about meanings, responsibility to learn and use concepts, etc. This is fairly

To apply promises to the representation of knowledge itself, we have to think more abstractly. Each topic, or item of knowledge, will be an agent that can make promises. The promises a

According to the rules above, no one else can make these promises. An example of the latter

• Every time we propose to know something, the inevitable uncertainty on the part of the involved parties (promiser and the promisee), means that the assertion at best has the

• Such a promise, once made, becomes a form of meta-information that describes the

We might write the above in a formal promise language, just to drive the point home:

comment => "Incomplete information about something", association => a("is a basic assumption of","promises");

association => a("may be viewed as","meta-information");

viewpoint, the scope can have an important influence on behaviour.

comment => "Information about some other information"; Promises themselves constitute information and hence may be perceived as a kind of knowledge; moreover promises may be about knowledge itself. Conversely, knowledge about a promise can influence the behaviour of agents who are not the intended promisee, so knowledge about knowledge can be discussed with promises. From an engineering

• A topic promises a brief explanation of itself, in a given context.

• A topic promises to be *associated* with another topic in a particular way. • A topic promises references *occurrences* of information that are about itself.

comment => "A book from classic Greek literature",

"Homer" comment => "A Greek writer from around 850 BC",

"Homer" comment => "Usually refers to Homer Simpson";

structure of knowledge, and is thus useful for analysis and reasoning.

unambiguous.

topics:

literature::

authors::

television::

topics: knowledge:: "uncertainty"

"promises"

"meta-information"

topic can make are like the following:

"Odyssey" # The topic/promiser

association => a("was written by","Homer");

association => a("wrote","Odyssey");

Promises therefore have two relationships to knowledge:

status of a promise, not a fact.

might be written like this:

• I promise that chapters consist of persons, in the context of religious orders.

These are promises about what an agent thinks he/she/it knows. Does anyone actually know these things for certain, i.e. are they facts? I think not. When, as individuals, we say 'a chapter is a part of a book' this is a confident statement, steeped in self-assurance. Anyone haunted by a modest amount of scientific humility would be less unilateral in making this kind of assertion and would probably try to qualify it with all kinds of uncertainty. Of course, what we mean is: to the best of my knowledge and belief, this knowledge is correct – please trust me, see Bergstra & Burgess (2006). We must not call these statements facts, but rather subjective expressions that might or might not be confirmed by others' viewpoints, thus essentially a promise.

Promises themselves constitute what we think we know, but they also discuss another level of knowledge. By thinking of knowledge as something that agents (i.e. you and I) have to promise to know, we will be able to reverse the simple design error in modern information systems that assumes authoritativeness, and turn logical ontology databases (designed by committees) back into simple language and hearsay that ordinary people own.

#### **5.2 Basic principles of promises**

There are two ways in which we are going to apply promises in this discussion: as a model of the people interacting with knowledge, and as a model of the knowledge itself. I will use the term 'topic' as a shorthand for knowledge item. Moreover, there are two kinds of promises that we should mention:


Both underline the individuality or autonomy of an agent, and we can use the term voluntary cooperation to express this freedom to disbelieve or not accept.

In the promise theory view, a promisee is not obliged to accept something promised by another agent. He or she must promise to accept a given promise in order for communication to be complete.

The basic principles of promises may be applied as follows, see Bergstra & Burgess (1994-now):


Each topic is therefore self-contained, independent and can be unique in an individual's context, while still allowing for multiple interpretations. This allows multiple, private viewpoints.

#### **5.3 Applying promises to knowledge indexing**

The modern approach to category design is grounded in the goal of making sophisticated indices to make information accessible and to codify experience. To discuss how humans interact with knowledge, we shall make people (i.e. the users of knowledge) into the role of 6 Will-be-set-by-IN-TECH

These are promises about what an agent thinks he/she/it knows. Does anyone actually know these things for certain, i.e. are they facts? I think not. When, as individuals, we say 'a chapter is a part of a book' this is a confident statement, steeped in self-assurance. Anyone haunted by a modest amount of scientific humility would be less unilateral in making this kind of assertion and would probably try to qualify it with all kinds of uncertainty. Of course, what we mean is: to the best of my knowledge and belief, this knowledge is correct – please trust me, see Bergstra & Burgess (2006). We must not call these statements facts, but rather subjective expressions that might or might not be confirmed by others' viewpoints, thus essentially a

Promises themselves constitute what we think we know, but they also discuss another level of knowledge. By thinking of knowledge as something that agents (i.e. you and I) have to promise to know, we will be able to reverse the simple design error in modern information systems that assumes authoritativeness, and turn logical ontology databases (designed by

There are two ways in which we are going to apply promises in this discussion: as a model of the people interacting with knowledge, and as a model of the knowledge itself. I will use the term 'topic' as a shorthand for knowledge item. Moreover, there are two kinds of promises

Both underline the individuality or autonomy of an agent, and we can use the term voluntary

In the promise theory view, a promisee is not obliged to accept something promised by another agent. He or she must promise to accept a given promise in order for communication

The basic principles of promises may be applied as follows, see Bergstra & Burgess

1. Anything that can be independently expressed or can change in some way can be an agent (promiser), entitled to its own viewpoint, and can make promises about its condition. We use this to say that any topic we can think of is assumed to exist and can promise to be

2. An agent (promiser) can only make promises about itself, not about other agents. Thus, in our case, a topic is only responsible for what it claims to know about itself and how it

Each topic is therefore self-contained, independent and can be unique in an individual's context, while still allowing for multiple interpretations. This allows multiple, private

The modern approach to category design is grounded in the goal of making sophisticated indices to make information accessible and to codify experience. To discuss how humans interact with knowledge, we shall make people (i.e. the users of knowledge) into the role of

• I promise that chapters consist of persons, in the context of religious orders.

committees) back into simple language and hearsay that ordinary people own.

• Promises to offer or give something, e.g. I claim to be an expert in Foo.

• Promises to use or accept something, e.g. I believe your claim.

cooperation to express this freedom to disbelieve or not accept.

related to any other, in the mind of an agent.

**5.3 Applying promises to knowledge indexing**

claims to relate to other topics.

promise.

**5.2 Basic principles of promises**

that we should mention:

to be complete.

(1994-now):

viewpoints.

promise-agents, in the first case. They will make certain promises to one another, which will involve agreeing about meanings, responsibility to learn and use concepts, etc. This is fairly unambiguous.

To apply promises to the representation of knowledge itself, we have to think more abstractly. Each topic, or item of knowledge, will be an agent that can make promises. The promises a topic can make are like the following:


According to the rules above, no one else can make these promises. An example of the latter might be written like this:

```
topics:
 literature::
    "Odyssey" # The topic/promiser
        comment => "A book from classic Greek literature",
    association => a("was written by","Homer");
 authors::
    "Homer" comment => "A Greek writer from around 850 BC",
        association => a("wrote","Odyssey");
television::
    "Homer" comment => "Usually refers to Homer Simpson";
```
Promises therefore have two relationships to knowledge:


We might write the above in a formal promise language, just to drive the point home:

```
topics:
 knowledge::
   "uncertainty"
        comment => "Incomplete information about something",
    association => a("is a basic assumption of","promises");
   "promises"
      association => a("may be viewed as","meta-information");
    "meta-information"
          comment => "Information about some other information";
```
Promises themselves constitute information and hence may be perceived as a kind of knowledge; moreover promises may be about knowledge itself. Conversely, knowledge about a promise can influence the behaviour of agents who are not the intended promisee, so knowledge about knowledge can be discussed with promises. From an engineering viewpoint, the scope can have an important influence on behaviour.

And the Emergence of Ontology 9

What's Wrong with Knowledge Management? And the Emergence of Ontology 157

When we subdivide subject categories in the manner of a hierarchy, we are laying an artificial tree on top of the mesh network and pretending that the new shape is a good representation of the old shape (see fig. 2). It is clear to see that we can lay many different trees across

(a) (b)

a generalized set of topics, and so there is is no unique tree that represents a given set of

**Definition 5** (Spanning tree)**.** *Any DAG (tree) that starts from an arbitrary root node in a graph*

**Definition 6** (Singleton)**.** *An isolated node in a graph, unconnected to any other. In our case, this is*

If we plot a frequency diagram of *n*(*k*), the number of nodes if degree *k*, this

In discussing what individuals know as a group, we will have use for the notion of agreement. The term agreement is often confused with 'contract' in economics and social sciences, but that is a derived meaning. We are only concerned with what agents claim to agree about. This is also a matter of promises, it turns out, because agents cannot know if they actually have the

Taxonomies have the property of a spanning tree (see sections below and fig. 3).

**Definition 7** (The degree of a node)**.** *Denoted k. The number of associations a node has.*

degree-distribution can be used to characterize the processes behind the structure.

Fig. 2. Illustration of a spanning tree (b) for a general network (a).

For the evaluation of graphs, let us define one more thing.

knowledge items.

*and covers all the nodes once only.*

**5.5 Agreement and consensus**

*a topic with no associations.*

Why introduce these notions? The most compelling answer is that this model has several useful properties: it defines atomic 'topics' with a minimum of assumed structure, but embodies some principles that preserve things we know to be true about knowledge: that knowledge is subjective in both content and structure, and is defined by a collaborative process by individuals.

#### **5.4 Graphical representations - knowledge maps**

Whenever things are interrelated, they form networks. In mathematics, the technical term for networks is *graphs*. The World Wide Web is one such network. There is a science of networks and their properties that has been studied at length in the literature, see Albert & Barabási (2002); Newman (2003). Since networks play such a large role in knowledge transfer, any theory of knowledge management must take their properties into account.

The agent making the promise (called the promiser) often directs it at a particular agent (one or more promisees), but others may also know about the promise. We call the set of agents who know about a promise the *scope* of the promise. There is thus communication from

$$promiser \to (promisee + scope)\tag{1}$$

We define a knowledge map as follows:

**Definition 4** (Knowledge Map)**.** *A directed graph* Γ(*T*, *A*)*, where T is a set of nodes representing topics, and A is a set of edges or links representing associations between topics.*

Fig. 1. Networks with different shapes: (a) A star network, (b) a tree, (c) a mesh.

Graphs or networks have many different shapes. If we begin to associate ideas freely, we end up with a 'mesh' (see fig 1 (c)). On the other hand, if we try to subdivide topics into categories in a 'branching process', we get a tree or hierarchy (see fig. 2 (b)). A tree is also called an acyclic graph (or DAG for Directed Acyclic Graph) because it contains no loops. A single category looks like the figure (a).

8 Will-be-set-by-IN-TECH

Why introduce these notions? The most compelling answer is that this model has several useful properties: it defines atomic 'topics' with a minimum of assumed structure, but embodies some principles that preserve things we know to be true about knowledge: that knowledge is subjective in both content and structure, and is defined by a collaborative

Whenever things are interrelated, they form networks. In mathematics, the technical term for networks is *graphs*. The World Wide Web is one such network. There is a science of networks and their properties that has been studied at length in the literature, see Albert & Barabási (2002); Newman (2003). Since networks play such a large role in knowledge transfer, any

The agent making the promise (called the promiser) often directs it at a particular agent (one or more promisees), but others may also know about the promise. We call the set of agents who know about a promise the *scope* of the promise. There is thus communication from

**Definition 4** (Knowledge Map)**.** *A directed graph* Γ(*T*, *A*)*, where T is a set of nodes representing*

(a) (b) (c)

Graphs or networks have many different shapes. If we begin to associate ideas freely, we end up with a 'mesh' (see fig 1 (c)). On the other hand, if we try to subdivide topics into categories in a 'branching process', we get a tree or hierarchy (see fig. 2 (b)). A tree is also called an acyclic graph (or DAG for Directed Acyclic Graph) because it contains no loops. A single

Fig. 1. Networks with different shapes: (a) A star network, (b) a tree, (c) a mesh.

*promiser* → (*promisee* + *scope*) (1)

theory of knowledge management must take their properties into account.

*topics, and A is a set of edges or links representing associations between topics.*

process by individuals.

**5.4 Graphical representations - knowledge maps**

We define a knowledge map as follows:

category looks like the figure (a).

When we subdivide subject categories in the manner of a hierarchy, we are laying an artificial tree on top of the mesh network and pretending that the new shape is a good representation of the old shape (see fig. 2). It is clear to see that we can lay many different trees across

Fig. 2. Illustration of a spanning tree (b) for a general network (a).

a generalized set of topics, and so there is is no unique tree that represents a given set of knowledge items.

**Definition 5** (Spanning tree)**.** *Any DAG (tree) that starts from an arbitrary root node in a graph and covers all the nodes once only.*

Taxonomies have the property of a spanning tree (see sections below and fig. 3).

**Definition 6** (Singleton)**.** *An isolated node in a graph, unconnected to any other. In our case, this is a topic with no associations.*

For the evaluation of graphs, let us define one more thing.

**Definition 7** (The degree of a node)**.** *Denoted k. The number of associations a node has.*

If we plot a frequency diagram of *n*(*k*), the number of nodes if degree *k*, this degree-distribution can be used to characterize the processes behind the structure.

#### **5.5 Agreement and consensus**

In discussing what individuals know as a group, we will have use for the notion of agreement. The term agreement is often confused with 'contract' in economics and social sciences, but that is a derived meaning. We are only concerned with what agents claim to agree about. This is also a matter of promises, it turns out, because agents cannot know if they actually have the

And the Emergence of Ontology 11

What's Wrong with Knowledge Management? And the Emergence of Ontology 159

Animal

Bird Man Fish

Black Brown Grey Cod Octopus Starfish Whale Fig. 3. A taxonomy usually has the structure of a tree - which has the technical name of Directed Acyclic

Since taxonomies are non-unique (they are merely arbitrary spanning trees, viewed from a particular starting point), they cannot be considered fundamental. They are design structures, often decided by committees or standardizing bodies, in order to achieve a consensus. Taxonomies are about putting things in one special box with a special name. A less contrived structure approach to classification is to use a network representation where names are less important than qualities. Semantic webs, or the ontologies they represent, are designed to allow this by letting anything associate itself with anything else, without due regard for

**Definition 12** (Ontology)**.** *A mesh of topics and categories, supplemented by promises of associations*

The state of IT ontologies today borrows a lot from taxonomy, in that it is usual for concepts or topics still to be arranged into hierarchical boxes. In this respect, ontology has been unable to divorce itself from hierarchical taxonomy. This seems to be more a case of habit than actual judgement – a hangover from computer science's database modelling and object orientation doctrines. However, and I would like to propose that this causes more problems than it solves.

If taxonomy and ontology pretend authority from arbitrariness, what might then be fundamental? Promise theory emphasizes that 'fundamental' is a subjective issue: it belongs to a specific agent's point of view, which in turn is limited by what information is available to it. If we are to build something fundamental, we must therefore base it on subjective viewpoints. Indeed, it suggests that everyone has to build their own viewpoint. This is what

A set of associations can just as effectively capture worthwhile aspects of hierarchy, by documenting which concepts are generalizations of others. For example 'animal' is sometimes

ontologies try to achieve and fail at because they retain a notion of authority.

Graph (DAG).

*to other topics.*

boundaries, by using flexible associations.

**6.2 Survivability of an ontology**

same understanding as any other. At best they can promise to agree to something given their best understanding.

Agreement is what happens when two or more agents seem to arrive at a common understanding. For example, two parties can agree that 2 + 2 = 4. Two agents may or may not know about their common state of agreement. By promising to agree, they can make this public.

**Definition 8** (Proposal)**.** *A proposal P is a prototype promise, i.e. the statement of an intention that has not yet been been made public, or acted upon.*

**Definition 9** (Agreeing)**.** *Agents are said to agree about a proposal P, if they independently promise to adopt P i.e. if they formally promise to use the proposal and make the promise themselves.*

Agreement is the basis of most of cooperation, and it is the way in which agents arrive at a common understanding. It is therefore central to knowledge management.

Agreement, about some body of information, can thus be viewed as a number of promises. In a contract, for example, one writes down a number of proposals for each side to follow (which are themselves prototype promises), and then the parties promise to subscribe to these by signing. If all parties promise that a set of proposals will be honoured, then an agreement may be expressed as a promise to keep some specification or promise proposals. This may be called the *body* of the agreement. The term contract is also used here.

**Definition 10** (Agreement)**.** *A promise agreement is a pair of use-promises between two parties to acknowledge and adopt the body of a proposal.*

Now, armed with this briefest introduction to the promise model, we can get back to the main story: knowledge.

#### **6. Knowledge maps**

#### **6.1 Some definitions**

To speak of a technology for knowledge maps, we need to formulate a more precise and technical definition of categories, using a promise model. Let's begin with some core concepts.

**Definition 11** (Taxonomy)**.** *A hierarchy of topics organized into categories in the manner of a tree with parent (container) concepts and children belonging to parent-categories.*

Taxonomy has been one of the main tools for classifying 'things' in the world, especially in biology. It is a way of putting things, concepts and ideas into one and only one box. Because every concept can only belong to a unique part of the tree hierarchy, the structure of knowledge is fragile to mistakes in choosing the wrong categories for something. Because everything that follows depends on the decisions made, making a change to a tree can be an expensive operation that requires unpicking and redesigning. Moreover, trees are branching processes, they tend to lead to too many different locations for information to reside, and trade complexity of information for complexity of categorization3.

<sup>3</sup> I often refer to this as the depth versus breadth problem. If we try to hide complexity inside category containers, and sub-containers we simply turn a one dimensional list into a two-dimensional structure, but the total number of things one has to deal with can still be the same, depending on the effectiveness of the categories.

10 Will-be-set-by-IN-TECH

same understanding as any other. At best they can promise to agree to something given their

Agreement is what happens when two or more agents seem to arrive at a common understanding. For example, two parties can agree that 2 + 2 = 4. Two agents may or may not know about their common state of agreement. By promising to agree, they can make this

**Definition 8** (Proposal)**.** *A proposal P is a prototype promise, i.e. the statement of an intention that*

**Definition 9** (Agreeing)**.** *Agents are said to agree about a proposal P, if they independently promise*

Agreement is the basis of most of cooperation, and it is the way in which agents arrive at a

Agreement, about some body of information, can thus be viewed as a number of promises. In a contract, for example, one writes down a number of proposals for each side to follow (which are themselves prototype promises), and then the parties promise to subscribe to these by signing. If all parties promise that a set of proposals will be honoured, then an agreement may be expressed as a promise to keep some specification or promise proposals. This may be

**Definition 10** (Agreement)**.** *A promise agreement is a pair of use-promises between two parties to*

Now, armed with this briefest introduction to the promise model, we can get back to the main

To speak of a technology for knowledge maps, we need to formulate a more precise and technical definition of categories, using a promise model. Let's begin with some core concepts.

**Definition 11** (Taxonomy)**.** *A hierarchy of topics organized into categories in the manner of a tree*

Taxonomy has been one of the main tools for classifying 'things' in the world, especially in biology. It is a way of putting things, concepts and ideas into one and only one box. Because every concept can only belong to a unique part of the tree hierarchy, the structure of knowledge is fragile to mistakes in choosing the wrong categories for something. Because everything that follows depends on the decisions made, making a change to a tree can be an expensive operation that requires unpicking and redesigning. Moreover, trees are branching processes, they tend to lead to too many different locations for information to reside, and trade

<sup>3</sup> I often refer to this as the depth versus breadth problem. If we try to hide complexity inside category containers, and sub-containers we simply turn a one dimensional list into a two-dimensional structure, but the total number of things one has to deal with can still be the same, depending on the effectiveness

*to adopt P i.e. if they formally promise to use the proposal and make the promise themselves.*

common understanding. It is therefore central to knowledge management.

called the *body* of the agreement. The term contract is also used here.

*with parent (container) concepts and children belonging to parent-categories.*

complexity of information for complexity of categorization3.

best understanding.

*has not yet been been made public, or acted upon.*

*acknowledge and adopt the body of a proposal.*

story: knowledge.

**6. Knowledge maps 6.1 Some definitions**

of the categories.

public.

Fig. 3. A taxonomy usually has the structure of a tree - which has the technical name of Directed Acyclic Graph (DAG).

Since taxonomies are non-unique (they are merely arbitrary spanning trees, viewed from a particular starting point), they cannot be considered fundamental. They are design structures, often decided by committees or standardizing bodies, in order to achieve a consensus.

Taxonomies are about putting things in one special box with a special name. A less contrived structure approach to classification is to use a network representation where names are less important than qualities. Semantic webs, or the ontologies they represent, are designed to allow this by letting anything associate itself with anything else, without due regard for boundaries, by using flexible associations.

#### **Definition 12** (Ontology)**.** *A mesh of topics and categories, supplemented by promises of associations to other topics.*

The state of IT ontologies today borrows a lot from taxonomy, in that it is usual for concepts or topics still to be arranged into hierarchical boxes. In this respect, ontology has been unable to divorce itself from hierarchical taxonomy. This seems to be more a case of habit than actual judgement – a hangover from computer science's database modelling and object orientation doctrines. However, and I would like to propose that this causes more problems than it solves.

#### **6.2 Survivability of an ontology**

If taxonomy and ontology pretend authority from arbitrariness, what might then be fundamental? Promise theory emphasizes that 'fundamental' is a subjective issue: it belongs to a specific agent's point of view, which in turn is limited by what information is available to it. If we are to build something fundamental, we must therefore base it on subjective viewpoints. Indeed, it suggests that everyone has to build their own viewpoint. This is what ontologies try to achieve and fail at because they retain a notion of authority.

A set of associations can just as effectively capture worthwhile aspects of hierarchy, by documenting which concepts are generalizations of others. For example 'animal' is sometimes

And the Emergence of Ontology 13

What's Wrong with Knowledge Management? And the Emergence of Ontology 161

It is not easy to come up with a universal measure of value for knowledge, of course, but in the spirit of modelling that is what we are going to pretend. Since there is no standard for this,

**Definition 13** (The value of a promise)**.** *Let V*(*π*) *denote a function, which when applied to a promise π returns a real number with the interpretation of a payoff/return. A negative value represents*

Let us suppress, for now, the issue of when the payoff occurs, and assume that keeping the promise leads to rewards that we are accounting for far off in the future, at some final reckoning. We may add up values over the time each time the status of a promise is assessed,

However we choose to account for these values, they are perceived very clearly in our minds

The hypothesis I propose is then that it must be possible to increase the net value of knowledge

*• The cost of knowledge assimilation can be reduced by avoiding knowledge management overheads.* The cost of knowledge includes the work done acquiring, using, transmitting it, etc. If we use accounting terminology, it is a simple matter of time and materials. The cost of information retrieval or lookup by either a person or a computer is related to the algorithmic complexity of the search required to find it. This translates most likely into the time a user has to invest

The costs are probably quite different depending on the representation of the knowledge items one uncovers while searching. For instance a book is likely easier to interpret than a painting, As a general question we would like to know how such costs that inhibit the learning and

Classification into categories plays a central role in this essay because it has been such an important activity in knowledge engineering for centuries. Why is it so popular? It was introduced in order to cluster together books about similar subjects. The contention is that this reduces tangibly the cost of finding relevant information, assuming that you understand

Suppose there are *T* topics divided into *C* categories. In a linear search (starting at the beginning and running through until we find the right one). If there is only one category,

> *<sup>χ</sup>*<sup>1</sup> <sup>=</sup> <sup>1</sup> 2

<sup>4</sup> Librarians are trained knowledge engineers who learn classifications and became smart assistants.

*T* (2)

then the cost of finding a topic on average is about half the length of the search list:

*• The value of information rises when accompanied by bonus associations to related topics.*

**7.1 The value of promised knowledge**

*a net cost associated with keeping the promise.*

when we interact with knowledge.

by adopting some simple strategies.

in accessing information.

**7.2 The cost of categories**

dissemination of knowledge can be reduced.

the classification in the first place4. Let us test this idea.

with values for:

we are free to invent one based on the promise model.

1. A promise found to be kept, positive if good or negative if bad. 2. Costs associated with keeping the promise (always negative). 3. The promise was not kept leads to a possible loss for the promisee.

**Hypothesis 1** (Increase value and reduce cost)**.** *Some principles: • Knowledge is made more accessible by reducing the cost of lookup.*

the right generalization for 'bird' or 'fish', but not always. It is also true that information classified as applying in any context generalizes information true only in a special context. These are set relationships, not hierarchical ones. With a 'generalization promise', any topic can bear allegiance to a number of possible generalizations in different contexts, without pre-designed limitations.

The survival of an ontology depends on its being used, which in turn depends on the agreement of all agents: i.e., keeping the promises to use the terms correctly, by all those who participate in it.

How does one know if a taxonomy (or technical ontology) has been a success? In our promise approach, we can simple measure this as the extent to which users keep the promises to support the ontology.
