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

Mark Burgess *CTO CFEngine Norway*

#### **1. Introduction**

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Knowledge Management (KM) is undoubtedly the challenge of this decade, and it is destined to shape the way we go about a wide range of fields of human activity for decades to come. Yet, while technologies claiming to enable KM abound, there is little sign that any wide reaching principles have been clearly understood or articulated, or that current research approaches have any positive benefit beyond brute force searching for answers, see Hicks et al. (2006). Nor is there any realistic alternative to the two major approaches to information organization: random search and retrieval (indexing), versus catalogue classification (the directory or table of contents).

In this essay, I should like to discuss some of the principles of knowledge organization, as I see them, from a perspective that has yielded some success in the related area of configuration or pattern management, see Bergstra & Burgess (1994-now); Burgess (2005). In order to keep things concise and focused, I will concentrate on spelling out a few specific criticisms of current approaches to KM, and then go on to propose adjustments to these approaches that could lead to large improvements in the current state of the art. Finally I'll set some challenges for future investigation.

#### **2. Background**

Knowledge management, or knowledge engineering (KE) today conjours up associations like: database, catalogue, ontology, semantic reasoning, etc. Yet, before information technology (IT) arrived on the scene, thousands of years of human development came up with quite different answers to the problem of passing on knowledge:


Only with the arrival written word, see Wolf (2007), did libraries begin to consider ways of managing large amounts of information, to accumulate knowledge and set about the task of organizing it. Today, however, those who work with knowledge (knowledge engineers, if we may call them that) feel that there is no mileage in these simple matters and are now only concerned with stockpiling and organizing information, then retrieving it, assuming that its simple existence as some form of documentation is enough to guarantee its usefulness.

And the Emergence of Ontology 3

What's Wrong with Knowledge Management? And the Emergence of Ontology 151

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

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

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.

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

**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

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

something from this.

annotation.

context.

**4. Knowledge representations**

thought of as different representations for knowledge.

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

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


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

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 domain knowledge.

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 with a knowledge base.
