Ontologies and Technologies: Knowledge
Representation or Misrepresentation
Kieron O’Hara
Intelligence, Agents, Multimedia Group
School of Electronics and Computer Science
University of Southampton
Highfield
Southampton SO17 1BJ
United Kingdom
kmo@ecs.soton.ac.uk
The development of the Semantic Web (SW) raises a number of difficult andinteresting technical issues. Less often remarked, however, are the social and politicaldebates that it will engender, if and when the technologies become widely accepted.
As the SW is a technology for transferring information and knowledge efficiently andeffectively, then many of these questions have an epistemological base. In this paper I
want to focus especially on the epistemological underpinnings of these social issues,
to think about the interaction between the epistemological and the political. How does
technology affect the social networks in which it is embedded? How can technology
be successfully transplanted into a new context? And, perhaps most importantly forus, how is technology affected by its context? In particular, I want to look at how ourdecisions about how we treat knowledge can impact quite dramatically on thetechnologies we produce.
Let us begin with a familiar diagram, the layered view of the SW developed in thevery early stages by Tim Berners-Lee and colleagues (Figure 1). Knowledge ofdifferent types is separated out (ontologies appearning in the middle). And by making
this separation, we can see how the semanticity of the Semantic Web comes about.
The technical machinery of Unicode and URIs appears at the bottom; XML can beused to tell the computer what objects this code is concerned with. RDF tells us thatthose objects are related in various ways. Ontologies give a context for the object
types and relation types. We then need logic and inference engines to make contextsensitive inferences over our knowledge bases. Proof theory tells us the properties of
our inference engines – soundness and completeness etc. And finally, there’s no point
being proven innocent if no one trusts the method of proof.

Figure 1: The layered view of the SW

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Hence, ontologies are central to the vision of information aggregation andmanipulation underlying the Semantic Web. Ontology-mediation within services
enables much to happen; they can steer the knowledge acquisition process,choreograph the integration of information from diverse sources and representational
formats, assemble retrieved information into customised packages, and hence present
information to the right people in the right form, bring intelligence to the search
process, undercutting the direct human input to the drudge work.
An example of an ontology-mediated system which exploits the new expressivity that
the SW allows is the winner of the 2003 Semantic Web Challenge, CS AKTive Space(Shadbolt et al 2004), which gives an up-to-date snapshot of the state of the disciplineof computer science in Britain. But the point of view of the snapshot is determined by
the user. In a massive knowledge acquisition exercise, various different technologies
assemble giant quantities of research-related information from the websites ofcomputer science departments, the EPSRC and so on. This is all collected on a regularbasis, and converted into RDF. The RDF store contains tens of millions of triples, andthe space enables the user to query that store to ask specific questions about British
computer science. Who is working on what? Who is working with whom? Where are
the geographical concentrations of researchers? Which areas are receiving thefunding? Who are the top people in different areas?
Ontology mediation is central to this flexibility in the presentation and selection of
information; how does this impinge on the social aspects of the SW?
There are two ways in which the Semantic Web can be seen as social. The first is that,
as we ascend Figure 1, the layers become increasingly socially rooted. In other words,
even though all of these layers are amenable to technical solutions (even trust, a social
phenomenon par excellence, can be addressed technically, as we see, of course, in theagents community, and increasingly in Semantic Web services), as we move up, eachlayer is more beholden to social phenomena. No matter how good the technicalsolutions provided, as we move along the direction of the arrow, those solutions must
respect more, and more complex, social phenomena. They can be criticised bypointing out that, no matter how impressive the solution, it fails to capture some vital
aspect of reality.
The other social aspect of the SW is common to all technologies: it exists in a context,
and is intended to move in other contexts. It will be crucially affected bydevelopments and demands from the economic world, information security andassurance, defence and government, organisational and managerial innovations, theentertainment industry, and, not least, academe. We have already seen this social
influencing with the World Wide Web. The Web was an interesting space for action,and developed accordingly, but when people wanted to use their credit cards online,
suddenly a new set of research imperatives was created, to do with security andidentity verification. We must expect analogous events in the future of the SW.
We must also not forget that these influences are two-way. Technologies aretransformative, and the Semantic Web may well affect the structures and practices inall these areas.
If we take these social aspects of knowledge seriously, then we need to move awayfrom traditional epistemology (O’Hara 2002). In this view, knowledge is a kind of
Platonic entity. We can “tap into it” when we have some kind of knowledge system,
be it organisational information processing, or machine readable repository, or just in

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our ordinary educational learning. In other words, knowledge is abstract, and then is
concretely represented more or less well. On this view, we might see the ideal
knowledge transfer process as like a frictionless plane, where no content is lost
through the transfer process, and the Internet as some superconducting medium which
can approximate that.
We need to switch to a more materialist view, where what we expect is a series ofmore or less costly translations from form to form, representative mode torepresentative mode (O’Hara 2002). Each translation from form to form may well lose
content, or involve generating new knowledge. Rightness or wrongness of translation
is a factor, but so is correctness for the task. In other words, knowledge manipulationis conceived of as a material process, using resources. Friction cannot be avoided,even on the Internet (Fuller 2002).
What we are talking about is the codification of knowledge. Knowledge has certain
properties; there are things it can do in the world, depending on how it is represented,
and what institutional forms lie behind it. Codifying it in a certain way will affectthose properties. For example, one radical type of codification is what the knowledge
management theorists Nonaka and Takeuchi call externalisation (Nonaka & Takeuchi1995). This takes tacit knowledge, know-how or knowledge that people possess but
has not been written down, and explicitly creates a codified, or explicit, version of theknow-how. We can study the effects of codification using externalisation as acommon type of example.
There are many advantages of codification, of tailoring knowledge to one’sorganisational needs: it is easier to move knowledge around the organisation; sharing
and retrieving it is much simpler; you don’t need a person who actually has thisknowledge tacitly in their head to be present. And when the owner leaves theorganisation, the knowledge stays behind, in the manuals, the computer, the Intranet,or wherever the explicit codification is held.
Furthermore, the codified knowledge has become an asset; it is now saleable. It can
act as the basis for a patent or copyright. The organisation can be granted monopoly
rights for a period over the knowledge, and if others want to use it they will need topurchase a licence.
And finally, codification converts knowledge from what economists call a rival goodto a non-rival good, i.e. it is now such that many people can use it at the same time.
When the knowledge was tacit, then the owner had to be present when the knowledge
was used. If he or she was needed at two places at once, they would have to prioritise,because tacit knowledge is a rival good. But once the knowledge is on the Intranet, forexample, different people can download it at the same time (Cowan et al 1999).
However, once we consider the social, economic and organisational consequences ofcodification, some disadvantages also appear. In the first place, codification isexpensive, it takes time, knowledge engineering, and so on. An expert is tied up
during knowledge acquisition.
Furthermore, whereas tacit knowledge tends to evolve naturally, explicitly written
down knowledge becomes ossified. It has to be maintained, kept up to date. That is allvery expensive, especially when the domain is very dynamic.
Thirdly, where trade secrets or the competitive advantage of confidentiality are
involved, a store of codified knowledge is much leakier. It can simply be emailed to arival, unlike the years of experience in a person’s head.

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And finally, explicit knowledge is much harder to apply than fully internalised
knowledge. Consulting a manual, or downloading material, is much more difficult.
Tacit knowledge often includes much about its own application. With explicit
knowledge, not only does the information have to be retrieved, but the user needs toknow which information is important, and where it can be found (Cowan et al 1999).
So codification of any kind usually involves big and important tradeoffs.
•

How is the knowledge to be secured? Do we want to go to law, get a patent? If
we do, then the knowledge is public, it can be reverse engineered. If not, wehave no comeback or protection? Do we need it?

•

How is the knowledge likely to be lost to the organisation? Will it leave with
its owner? Or is it more likely to be appropriated if we write it down?

•

What is the knowledge worth to us? If it is tacit, then we will ultimately haveto pay its owner more or less what it is worth to us. The labour market thus
becomes a substitute for a knowledge market. How should we maximise our
return on our knowledge acquisition or training expenditure?

•

And not least, who benefits from codification? If we codify tacit knowledge,
then we take much of its previous owner’s economic power away from him.
He used to be the monopoly supplier of that knowledge within theorganisation. But when we codify his knowledge, it is readily available. What
incentives could he possibly have for cooperating?

That brings us onto the economics of the situation. As we noted earlier, knowledge
acquisition – codification – is expensive, but the expenses vary depending on anumber of factors. Is there a code in place? Or will we have to create one? How
adequate are the available codes? Will they prevent the codification going well?
Is there a consensus in the domain, or are there disputed views? Particularly in large
corporations, different sectors, R&D, marketing, engineering, assembly, willconceptualise a domain very differently. If there is a dispute, then the operation ofcodification may involve taking sides. The chosen code may favour one side or theother. The person or team in charge of codification is at an enormous advantage inparticular if it is party to the dispute. This is particularly important with respect toontologies, which are supposedly shared conceptualisations of a domain. How are weto model disputes, when the actual content of the dispute may itself be disputed? Are
there authorities to whom we can turn? Who accepts their authority?
And there are many different types of knowledge. For example, it can be taxonomicor hierarchical, encoded in diagrammatic form, distributed across an organisation, or
it can be procedural knowledge. How can we deal with these different kinds ofknowledge? Are some trickier than others? Are some types so hard to model that ourmodels will always be too coarse and expensive (Brewster & O’Hara 2004)?
We can see some of these factors if we take an example from the early days of expert
systems. Problem-solving methods (PSMs) were recurrent patterns in expert
reasoning, that could be applied in different expert systems in different domains(Chandrasekaran 1983, Clancey 1985, Wielinga et al 1992). Such methods included
hierarchical classification, systematic diagnosis, and propose and revise design. They
could be used as skeletal expert systems, such that if they were filled in with domain
knowledge, they could be used for problem-solving. Similarly, they could direct
knowledge acquisition by telling the knowledge engineer what knowledge to acquire,O’HaraOntologies and Technologies

5and signposting what type of knowledge is required for problem-solving (O’Hara et al
1998, van Heijst 1995). In other words, the PSM contains within it ontological
specifications.
However, these specifications are highly task-specific. There is no guarantee thatthese ontological forms will be translatable for other tasks, or that the knowledge so
painfully acquired could be easily reused for another task. When knowledge has aprocedural element, there may well be difficulties in understanding how thatknowledge could be reused, even partially, for other tasks. In general, the lesson isthat certain types of knowledge (for example taxonomic knowledge) may well beeasier to put into ontologies than others.
Given these thoughts about codification, how do they leave our questions about the
Semantic Web and information retrieval? Are there questions raised by this whichhave yet to be properly addressed in the SW community?
Let’s look at how we might expect the SW to be used. Corporations, organisations use
knowledge technologies to help them make sense of a chaotic, uncertain world. They
want the best support for their decision-making and problem-solving. They often
operate in dynamic, disputed, contentious domains. They have only incomplete
information, often in inconvenient form. What they want from their technologies istimely interpretations of the evidence to let them make reliable decisions, and to letthem act effectively. Are ontologies the right things here?
They aren’t very good at representing disputes and divergence, since they are shared
and agreed conceptualisations of domains. They need to be available quickly as thedomain changes, but they are usually painstakingly hand-crafted. There is little
automation of development. They are hard to maintain. It is hard to remove out-ofdate information, because it is hard to work out what depends on what within an
ontology (Buckingham Shum 2004).
If we look at how organisations actually use ontologies, we find little consensus onwhat an ontology actually is. They tend to involve hierarchical taxonomies, but otherthan that, almost anything goes. What corporations tend to do with ontologies is tosystematise large volumes of knowledge, for example to do with research anddevelopment programmes. The ontology therefore looks like a hierarchical corporate
memory. Interestingly, knowledge is much more usually put in than retrieved.
Perhaps more interestingly, the big research issues as seen from industry arecorrespondingly different from those that excite the scientists. They include security
and ownership, trust, how to communicate to different audiences, the intended
audience in particular, and what media will be used for browsing and viewing. Hencethe major challenge from the corporate user’s point of view is not representational
adequacy, but rather the social context of use. How should this system integrate withother systems, computational or otherwise? How should content be acquired ordesigned (Ellman 2004)?
So when we look at the use of ontologies in the SW from the viewpoint of its social
context, we start to see a different set of problems emerging.
•

Which knowledge should we be representing? And how? And what are thelimits of our capabilities to do that? How disabling will those limits be?

•

Whose knowledge are we taking? Are we disturbing patterns of ownership,
redistributing power, legitimately?

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Should we try to represent dispute? How consensual should ontologies be?

•

Can there be personal ontologies, and what use would they be?

•

How do the costs of knowledge transfer get allocated in the new world? Who
pays?

•

And finally, what do we do about maintenance? Who is responsible for the
maintenance of knowledge repositories or ontologies? And how do we prevent
those people injecting their own biases into the mix?

To conclude, let us consider two models of economic activity (cf Fuller 2002, 36-44).
Profit seeking is the performance of work to realise future benefits; I invest my time
now to enable economic activity to take place in the future. This might be designing amachine, or writing some program, or inventing a procedure. Profit seeking, in otherwords, is productive. Contrast that with rent seeking, seeking benefit from past work.
The trick is to be in a position to prevent productive activity from taking place.
Corruption is one of the worst types of rent seeking, where an official, having
managed to get himself a key job in the past, now uses it to cream off bribes fromthose who need to get round the regulations. However, many types of rent seeking arelegitimate; nevertheless, they all flourish when productive activity can be retarded.
If we think about rent-seeking and profit-seeking in a knowledge market, we can see
two possible futures for the SW. The vision of Tim Berners-Lee, the vision that mostresearchers in the area have, is that of enabling new knowledge-based activity,
economic and otherwise, of promoting research, and of boosting the circulation of
information.
On the other hand, suppose rigid ontologies ossify outmoded conceptualisations of
domains. In that case, certain viewpoints within society, and within organisations,
may become privileged. They could use this privilege to their own advantage, and will
also act to defend those privileges. Knowledge is power, as they say, and if we finddifficulty representing all types of knowledge, we risk being party to a skewed andunproductive distribution of power.
That is not necessarily reason to be pessimistic that the vision of the SW will beeffaced by the nightmare. But we should be wary of the dramatic claims that somemake about the SW. We shouldn’t try to disguise the important social dimension toknowledge. We cannot adopt an epistemological stance free of politics.
What can we do in future? The main thing is to be alert to potential pitfalls of thetechnology. We can’t predict the future; technological determinism is always a big
mistake. But we must try to capture knowledge of diverse types from heterogeneous
sources, support reasoning that is as scruffy as we can bear, support the processes ofdebate and disagreement, and automate ontology construction, V&V, integration etc,
as far as possible.
These are all difficult and messy research problems. But the technology will benefit in
the end.
Acknowledgements
Much of this paper is informed by ‘Knowledge Representation with Ontologies: thePresent and Future’, a Trends and Controversies symposium edited by myself andChristopher Brewster for IEEE Intelligent Systems 19(1), Jan/Feb 2004, pp.72-81. The
authors in the symposium were Simon Buckingham Shum, Jeremy Ellman, Enrico

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Franconi, Steve Fuller, Mark A. Musen and Yorick Wilks, and I am pleased toacknowledge their influence on my ideas on this topic. This work is supported underthe Advanced Knowledge Technologies (AKT) Interdisciplinary Research
Collaboration (IRC), which is sponsored by the UK Engineering and PhysicalSciences Research Council under grant number GR/N15764/01. The AKT IRC
comprises the Universities of Aberdeen, Edinburgh, Sheffield, Southampton and theOpen University.
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