VocabControl

First we had content, then not long after that we had metadata, although no-one called it that. Now we need parametadata – the metadata about metadata!

Neither metadata nor parametadata are anything new, but what is new is how central they have become to all sorts of business processes. People think there is something modern and techie about metadata, but ever since the first author signed their initials on a piece of work, or added a title, we have had metadata. Librarians are just one group who have been using metadata for centuries.

Thanks to technological advances, there is now a huge amount of processing that can be done with metadata, indeed that needs to be done if we are to have any idea what assets we have available. Metadata has become the active driver of numerous business processes. You couldn’t operate a computer without the metadata that tells you the name of a file, its location, when it was last saved, etc. and this sort of metadata is so ubiquitous that nobody tends to think about it too much. Now metadata is so pervasive, it is becoming increasingly important to talk about it and define different aspects and types.

One key distinction is the one between objective and subjective metadata. Subjective metadata refers to classification, tagging, taxonomies, etc. This metadata is subjective because it is always possible to argue about it. Objective metadata on the other hand is uncontroversial and typically process-driven – a file format is what it is, the time the file was last saved might cause consternation after a PC crash, but is unarguable. However, there is actually surprisingly little uncontroversial metadata. Even something like a title can be edited and changed – what do you do when some content acquires a popular or folk title that is not the same as its official title? This happens a lot with comedy sketches and songs, but can also happen to names of projects, working groups, etc.

Parametadata (or meta-metadata) is another subset of metadata – it is the metadata about the metadata, giving its provenance, date of creation, technical specifications, etc. Once you start to think about metadata as content in its own right, it becomes obvious that just as you wish to track the author, title, and so on of the core content, so too you need to track the author(s), provenance, date of creation and latest update of the metadata as well. For subjective metadata, parametadata becomes hugely useful. Because you can have multiple classifications of an asset, it is very important to track the source – distinguishing between author added keywords, indexer keywords, and folksonomic tags, for example – so that people can tell where a tag has come from.

As long as you know where tags have come from, you can decide whether or not you want to trust in their authority. In an increasingly muddled web, it is helpful to be told the source of a comment or an opinion in order to try to distinguish sound information from propaganda or uninformed speculation. Anecdotally, many people who were initially excited about citizen review sites – rating hotels, etc. – have now given up on them on the grounds that the people who contribute to them tend to have some kind of axe – or worse – to grind, so you can’t take them seriously. Even reviews that aim to be fair may not be relevant if the reviewer is too dissimilar to the reader. The perfect holiday for a group of teenagers is unlikely to be what a retired couple are looking for. So any review needs to carry sufficient information so that the reader can work out how relevant the content is to them. A good review site would carry a range of reviews aimed at different audiences.

Similarly, a rich navigation system needs to offer a range of tags and taxonomies, but these will only be useful when there is sufficient parametadata to tell the user where each scheme or tag came from, who created it, how up to date it is, etc. From a user perspective, being able to choose from a range of well-documented navigation systems means they can make an informed choice about whether to have fun with the randomness of folksonomic tags, to follow a specialist taxonomy in order to learn how a subject is handled by experts, or to use a guide constructed by the content curators for a general audience.

Interface designers can use the parametadata to make different sources of metadata distinct – with different visual or other cues, for example, to indicate different navigation environments. This means you can create a range of different “navigation worlds” and let your users wander to and fro while always making sure they know where – in terms of trust and authority – they are.

What is an ontology?
Ontologies are emerging from the techie background into the knowledge organisation foreground and - as usually happens - being touted as the new panacea to solve all problems from content management to curing headaches. As with any tool, there are circumstances where they work brilliantly and some where they aren’t right for the job.

Basically, an ontology is a knowledge model (like a taxonomy or a flow chart) that describes relationships between things. The main difference between ontologies and taxonomies is that taxonomies are restricted to broader and narrower relationships whereas ontologies can hold any kind of relationship you give them.

One way of thinking about this is to see taxonomies as vertical navigation and ontologies as horizontal. In practice, they usually work together. When you add cross references to a taxonomy, you are adding horizontal pathways and effectively specifying ontological rather than taxonomical relationships.

The flexibility in the type of relationship that can be defined is what gives ontologies their strength, but is also their weakness in that they are difficult to build well and can be time consuming to manage because there are infinite relationships you could specify and if you are not careful, you will specify ones that keep changing. Ontologies can answer far more questions than taxonomies, but if the questions you wish to ask can be answered by a taxonomy, you may find a taxonomy simpler and easier to handle.

What are the differences between taxonomies and ontologies?
A good rule of thumb is to think of taxonomies as being about narrowing down, refining, and zooming in on precise pieces of information and ontologies as being about broadening out, aggregating, and linking information. So, a typical combination of ontologies and taxonomies would be to use ontologies to aggregate content and with taxonomies overlaid to help people drill down through the mass of content you have pulled together.

Ontologies can also be used as links to join taxonomies together. So, if you have a taxonomy of regions, towns, and villages and a taxonomy of birds and their habitats you could use an ontological relationship of “lives in” to show which birds live in which places. By using a taxonomy to support the ontology, you don’t have to define a relationship between every village and the birds that live there, you can link the birds’ habitats to regions via the ontology and the taxonomy will do the work of including all the relevant villages under that region.

Programmers love ontologies, because they can envisage a world where all sorts of relationships between pieces of content can be described and these diverse relationships can be used to produce lots of interesting collections of content that can’t easily be brought together otherwise. However, they leave it to other people to provide the content and metadata. Specifying all those relationships can be complicated and time-consuming so it is important to work out in advance what you want to link up and why. A good place to start is to choose a focal point of the network of relationships you need. For example, there are numerous ways you could gather content about films. You could focus on the actors so you can bring together the films they have appeared in to create content collections describing their careers, or focus on genres and release dates to create histories of stylistic developments, or you could link films that are adaptations of books to copies of those books. The choices you make determine the metadata you will need.

Know your metadata
At the moment, in practice, ontologies are typically built to string together pre-existing metadata that has been collected for navigational or archival taxonomies, but this is just because that metadata already exists to be harvested. There is a danger in this approach that you end up making connections just because you can, not because they are useful to anybody. As with all metadata-based automated systems, you also need to be careful with the “garbage in garbage out” problem. If the metadata you are harvesting was created for a different purpose, you need to make sure that you do not build false assumptions about its meaning or quality into your ontology - for example, if genre metadata has been created according to the department the commissioning editor worked for, instead of describing the content of the actual programme itself. That may not have been a problem when the genre metadata was used only by audience research to gather ratings information, but does not translate properly when you want to use it in an ontology for content-defining purposes.

Feeding your ontology with accurate and clearly defined taxonomies is likely to give you better results than using whatever metadata just happens to be lying about. Well-defined sets of provenance metadata – parametadata – about your taxonomies and ontologies is becoming more and more valuable so that you can understand what metadata sets were built for, when they were last updated, and who manages them.

Why choose one when you can have both?
Ontologies are very powerful. They perform different work to taxonomies, but ontologies and taxonomies can support and enhance each other. Don’t throw away your taxonomies just because you are moving into the ontology space. Ontologies can be (they aren’t always - see Steve’s comment below) big, tricky, and complicated, so use your taxonomies to support them.

This post is based on the notes I made for the talk I gave at the LIKE dinner on February 25th. It covers a lot of themes I have discussed elsewhere on this blog, but I hope it will be useful as an overview.

Taxonomies have been around for ages
Pretty much the oldest form of recorded human writing is the list, back in ancient Sumeria, the Sumerian King list for example is about 4,000 years old. By the time of the ancient Greeks, taxonomies were familiar. We understand that something is a part of something else, and the notion of zooming in or narrowing down on the information we want is instinctive.
I am frequently frustrated by the limitations of free text search (see my earlier post Google is not perfect). The main limitation is to knowledge discovery - you can’t browse sensibly around a topic area and get any sense of overview of the field. Following link trails can be fun, but they leave out the obscure but important, the non-commercial, the unexpected.

The very brilliant Google staff are working on refining their algorithms all the time, but Google is a big commercial organisation and they are going to follow the money, which isn’t always where we need to be going. Other free text search issues include disambiguation/misspellings – so you need hefty synonym control, “aboutness” – you can’t find something with free text search if it doesn’t mention the word you’ve searched for, and audio-visual retrieval. The killer for heritage archives (and for highly regulated companies like pharmaceutical and law firms) is comprehensiveness – we don’t just want something on the subject, we want to know that we have retrieved everything on a particular subject.

Another myth is that search engines don’t use classification – they do, they use all sorts of classifications, it’s just that you don’t tend to notice them, partly because they are constantly being updated in response to user behaviour, giving the illusion that they don’t really exist. What is Google doing when it serves you up its best guesses, if not classifying the possible search results and serving you the categories it calculates are closest to what you want?

I’m a big fan of Google, it’s a true modern cathedral of intellectual power and I use Google all the time, but I seem to be unusual in that I don’t expect it to solve all my problems.
I also am aware of the fact that we can’t get to look at Google’s taxonomic processes arguably makes Google more political, more manipulable, and more big brother-ish than traditional open library classifications. We may not totally agree with the library classifications nor the viewpoints of their creators, but at least we know what those viewpoints are!

There was a lot of fuss about the rise of folksonomies and free tagging as being able to supersede traditional information management – and in an information overloaded world we need all the help we can get – the trouble is that folksonomies expand, coalesce, and collapse into taxonomies in the end. If they are to be effective – rather than just cheap – they need to do this – and either become self-policing or very frustrating. They are a great way of gathering information, but then you need to do something with it.

Folksonomies, just as much as taxonomies, represent a process of understanding what everyone else is talking about and negotiating some common ground. It may not be easy, but it is a necessary and indispensable part of human communication - not something we can simply outsource or computerise – algorithms just won’t do that for us. Once everything has been tagged with every term associated with every viewpoint, nothing might as well have been tagged at all. Folksonomies, just as much as taxonomies, collapse into giving a single viewpoint – it’s just that it is a viewpoint that is some obscure algorithmic calculation of popularity.

So, despite free text search and folksonomies, structured classification remains a very powerful and necessary part of your information strategy.

It’s an open world
Any information system - whatever retrieval methods it offers - has to meet the needs of its users. Current users can be approached, surveyed, talked to, but how do you meet the needs of future users? The business environment is not a closed, knowable constrained domain, but is an “open world”1 where change is the only certainty. (Open world is an expression from logic. It presumes that you can never have complete knowledge of truth or falsity. It is the opposite of the closed world, which works for constrained domains or tasks where rules can be applied - e.g. rules within a database).

So, how do you find the balance between stability, so your knowledge workers can learn and build on experience over time, while being able to react rapidly to changes?

Once upon a time, not much happened
The early library scientists such as Cutter, Kelley, Ranganathan, and Bliss, argued about which classification methods were the best, but they essentially presumed that it was possible to devise a system that maximised “user friendliness” and that once established, it would remain usable well into the future. By and large, that turned out to be the case, as it took many years for their assumptions about users to be seriously challenged.

Physical constraints tended to dictate the amount of updating that a system could handle. The time and labour required to re-mark books and update a card catalogue meant that it was worth making a huge effort to simply select or devise a classification and stick to it. It was easier to train staff to cope with the clunky technology of the time than adapt the technology to suit users. No doubt in the future, people will say exactly the same things about the clunky Internet and how awful it must have been to have to use keyboards to enter information.

So, it was sensible to plan your information project as one big chunk of upfront effort that would then be left largely alone. It is much easier to build systems based on the assumption that you can know everything in advance - you can have a simple linear project plan and fixed costs. However, it is very rare for this assumption to hold for very long, and the bigger the project, the messier it all gets.

Change now, change more
Everything is changing far more rapidly than it used to – from the development of new technologies to the rapid spread of ideas promoted by the emergence of social media and an “always on” culture. It’s harder than ever to stay cutting edge!

We all like to speak our own language and use our own names for things, and specialists and niche workers as well as fashionistas and trendsetters expect to be able to describe and discuss information in ways that make sense to them. The open philosophy of the Web 2.0 world means that they increasingly take this to be their right, but this is where folksonomic approaches can really help us.

What you need to do is to create a system that can include different pace layers so that you get the benefits of a stable taxonomy, with the rapid reactiveness of folksonomy as well as quick and easy free text search. You can think of your taxonomy as the centre of a coral reef, but coral is alive and grows following the currents and the behaviour of all the crazy fish and other organisms that dart about around it. It’s hard to pin down the crazy fish and other creatures, but they feed the central coral and keep it strong. In practice, this means incorporating multiple taxonomies and folksonomies and mapping them to one another, so that everyone can use the taxonomy and the terminology that they prefer. Taxonomy mapping tools require human training and human supervision, but they can lighten the load of the labour intensive process of mapping one taxonomy to another.

This means that taxonomy strategy does not have to be determined at a fixed point, but taxonomy creation is dynamic and organic. Folksonomies and new taxonomies can be harvested to feed back updates into the central taxonomy, breaking the traditional cycle of expensive major revision, gradual decline until the point of collapse, followed by subsequent expensive major revision…

There is a convergence between semi-automated mapping (we’ll be needing human editorial oversight for some time) and the semantic web project. This is the realisation of the “many perspectives, one repository” approach that should get round many problems of the subjective/objective divide. If you can’t agree on which viewpoint to adopt, why not have them all? Any arguments then become part of the mapping process – which is a bit of a fudge, but within organisations has the major benefit of removing a lot of politicking that surrounds information and knowledge management. It all becomes “something technical” to do with mapping that nobody other than information professionals is very interested in. Despite this, there is huge cultural potential when it comes to opening up public repositories and making them interoperate. The Europeana project is a good example.

Modern users demand that content is presented to them in a way that they feel comfortable with. The average search is a couple of words typed into Google, but they are willing to browse if they feel that they are closing in on what they want. To increase openness and usage means providing rich search and navigation experiences in a user-friendly way. If your repository is to be promoted to a wider audience future, the classification that will enable the creation of a rich navigation experience needs to be put in place now.

Your users should be able to wander about through the archive collections horizontally and vertically and to leave and delve into other collections, or to arrive at and move through the archive using their own organisation’s taxonomy and to tag where they want to tag, using whatever terms they like. The link points in the mappings provide crossroads in the navigation system for the users.

In this way the taxonomies are leveraged to become “hypertextual taxonomies” that provide rich links both horizontally and vertically.

Taxonomy as a spine
A core taxonomy that acts as an indexing language is the central spine to which other taxonomies can be attached and crucially - detached - as necessary. The automation of the bulk of the mapping process means that incorporating a new taxonomic view
becomes a task of checking the machine output for errors. Automated mapping processes can provide statistical calculations of likelihood of accuracy and so humans only need to examine those with a low likelihood of being correct.

Mapping software has the same problems as autoclassification software, so a mapping methodology, including workflow and approval processes, has to be defined and supported. The more important it is to get a fine-grained mapping, the more effort you will need to make, but a broad level mapping is easier to achieve.

Conclusion
If you start thinking of the taxonomy as an organic system in its own right – more like an open application that you can interact with – bolting on and removing elements as you choose, you do not need to attempt to account for every user viewpoint in the creation of the taxonomy, and that omission of a viewpoint at one stage does not preclude that collection from being incorporated later. Conversely, the mapping process allows “outsiders” to view your assets through their own taxonomies.

Our taxonomies represent huge edifices of intellectual effort. However, we can’t preserve them in aspic – hide them away as locked silos or like grand stately homes that won’t open their doors to the public. If we want them to thrive and grow we need to open them up to the light to let them expand, change and interact with other taxonomies and take in ideas from the outside.

Once you open up your taxonomy, share it and map it to other taxonomies, it becomes stronger. Rather than an isolated knowledge system that seems like a drain on resources, it becomes an embedded part of the information infrastructure, powering interactions between multiple systems. It ceases to be a part of document management, and becomes the way that the organisation interacts with knowledge globally. This means that the taxonomy gains strength from its associations but also gains prestige.
So our taxonomies can remain our friends for a little while longer. We won’t be hand cataloguing as we did in the past because all the wonders of the Google and automated world can be harnessed to help us.

Why bother with information architecture? — RenaissanceCMS. I was happy to be asked to write something on information architecture generally for Rob’s blog. It’s easy to forget that not everyone takes for granted the usefulness of IA, so I have tried to inspire people who aren’t sure what it is or what it can do.

Rob creates charming ethereal designs as well as working on marketing, branding, and visual identity and being generally ethical and sustainable. I particularly liked his latest post on tagging. I tend to approach folksonomy from a management and retrieval point of view, and so find myself arguing that just because it is cheap, doesn’t mean it can replace all other KO systems. However, I have been thinking about image retrieval, and one area where social tagging is useful is in labelling vague and abstract ideas like “mood”. If most people tag a photo as “sad” or “mysterious”, that is probably going to be useful for creative people who don’t need a specific image but are just after something that evokes a “feeling”.

I was very pleased to be sent this link: Mozilla design challenge showcases new browser tab concepts - Ars Technica. The winner is a lovely hierarchical visualisation that could work really well with concept maps/visual thesauruses/taxonomies. It preserves parent/child relationships using a radial format, which is more flexible than traditional trees, in that you can follow several pathways at once and maintain an overview.

Content Strategy - a knol by Jeffrey MacIntyre - it sounds like I’ve been being a content strategist without realising it too!

Designing for faceted search - some clear “dos” and “don’ts” when constructing taxonomies to support faceted searching.

I haven’t read the book yet, but this blog post on screen templates presents 12 basic layouts and the sort of information they work best with. It could be a useful checklist if you want to manage or rationalise presentations across a large website, especially one that has evolved organically and could do with tidying up. The templates are simple (seasoned designers won’t find much they don’t know already) but could be handy for anyone new to web design and layout who wants some “off the peg” styles to get them started.

Thanks to Rey for the link!

I’ve been studying usability evaluation methods (UEMs), which although not directly related to taxonomy work, are relevant for anyone involved in information architecture (IA). I was surprised at how controversial a subject usability is, having assumed that everyone wants their sites to be as usable as possible. However, assessing usability does involve a lot of judgement calls and tradeoffs, which is one reason why some people seem to take against it.

You have to decide who you are going to focus your usability testing on, perhaps choosing a “core user group” rather than trying to please everybody. You have to decide what aspects of usability you are going to focus on - for example accessibility (everybody should be following minimum W3C standards anyway), but you might legitimately decide that you are not going to worry about making your site easy for children to read (e.g. if it is a postgraduate discussion forum). Then you need to decide if you are going to try to make individual tasks as efficient as possible (e.g. not using as many keystrokes) or look at the site as a whole (e.g. a social networking site might place a higher value on being fun and funky over being efficient to use).

Once you have decided who your target users are and what aspect of usability you are most interested in, you can choose a testing method. There seem to be over 100 different methods out there, ranging from fairly straightforward ones like Jakob Nielsen’s Heuristic Evaluation - which gives you a checklist of things to look at, and even “expert inspection” where you just look at the site to try to find potential problems. These methods assume you know quite a lot about what makes a site usable or not.

You could do an experiment, where you set up a task or scenario and measure people’s performance at it. This is often described as laboratory testing, but you can have a “lab” that is just you, a notebook, and a computer for your participants. This sort of test is great if you have one specific function (e.g. an ecommerce function) and you want to check that people can follow the steps easily.

The methods I liked the most were the more abstract conceptual methods, like CASSM, where you try to get a picture of users’ expectations and then compare them with the website to see where there are gaps or conflicts.

Interestingly, the literature shows that for all methods there is a marked “evaluator effect“, with different evaluators getting different results even when using an identical process. I think this is because there is so much interpretation at all stages. The closest you’d get to a “scientific” set of original data would be to set up a carefully controlled usability lab test, but even then translating the results into redesign suggestions is really an art, not a science.

There also seems to be a “political correctness gone mad” brigade who say that accessibility means you can’t have any pictures on your site and that Jakob Nielsen’s site looks horrible and out of date. I think this is a misunderstanding of what usability is all about. Usability is about making a site easier for everyone to use, and accessibility isn’t about leaving features out because certain people can’t use them, it’s about providing a “Plan B” for anyone who doesn’t use the site in the way you expect. So, it seems to me that it’s fine to including fancy visualisations, as long as you also provide a text description for people who can’t see them, or a tricksy javascript feature as long as you include an alternative for browsers that don’t have javascript enabled. Nielsen’s site is old fashioned, but that doesn’t mean it is the only way to create a usable site. The BBC have aesthetically pleasing modern sites that are also well crafted for accessibility.

It is true that there are tradeoffs and quite a lot of art rather than science in usability evaluation, but I think there is a moral (not to mention legal in the UK - not sure about elsewhere) imperative to at least try to be inclusive and in most cases it is simply poor marketing to shut out or make life difficult for potential customers.

I went on Tuesday to Online 2008 at Olympia. It seemed quieter than last year, so I took advantage of some of the free presentations. I listened to Laurent Le Meur talking about Agence France-Presse (AFP)’s efforts to create a multimedia news database - (Imageforum), Graham Beastall of Soutron Ltd on Taxonomy Development using Sharepoint, Scott Gavin on Knowledge Plaza, and Judith Lewis of i-level on the Dark Side of Social Media.

Le Meur described the need to create a common metadata language to bring together journalists and photographers, who tended to think about subjects very differently. AFP use autocategorisation software (supplied by Temis) but have invested heavily in training it to work well, in other words lots of human input. They imported a number of existing vocabularies from such sources as GeoNames, EuroVoc, and the IPTC’s taxonomy of news categories as a base, selecting 300 of the IPTC’s 1,300 categories to improve software performance. They currently extract and autocategorise people, organisations, locations, points of interest, products, and brands. They would realy like to be able to pick out news events, but language usage is too broad and diffuse for them to have managed this with any success.

Their documentalists and indexers were initially reluctant to work with the new system as it meant a dramatic reduction in the complexity of their indexing work. Previously they could use some 3,000 terms but this was reduced in order to be compatible with the entity extraction software.

For images, they found the key facets were expressions (e.g. smiling), action, aspect (e.g. profile, close-up) and style (e.g. backlit). They are happy with the Antidot faceted navigation system that allows them to choose index fields, but have not been able to incorporate image rights, as they are too complex and vary according to things like location of the user.

Beastall said that users of Sharepoint are not fully exploiting it, with only 4% using it as a tool for search, while 43% use it for collaborative working. He warned that you need to impose discipline in categorisation right from the start of an implementation as once information has “grown wild” it is far harder to retrospectively tidy it up. He also pointed out that people tend to think they know where to find things, but then someone else has a site reorganisation, so if key information wasn’t well indexed, it can be lost.

There is also value in segmenting your information so that you have public areas separate from the main enterprise content management system. Such public areas can then be treated differently in terms of things like security and social working. An interesting take on the taxo/folkso synergy is to let people build their own sites, but to have a central team looking out for good candidates for inclusion in centralised systems, and to bring personal sites in when they are useful, amalgamating to remove duplicates, etc. He encouraged the use of folksonomies as a “fast track” to sit beside the core vocabularies and feed into them, as folksonomies are particularly useful for new and fast-moving areas, but not so good for long term management and control. He cited the websites contentandcode.com - a Microsoft solutions provider, The Information Architecture Institute, a useful article on taxonomies, thesauruses, etc., by metamodel, and the Sharepoint blog vitalskill.com.

Knowledge Plaza is a CMS [Scott Gavin has pointed out that it is actually more a Knowledge Management/Enterprise Search tool - see his comment] that allows a dual taxo/folkso approach, with options for a totally open folkso system, a managed folkso system, where users can use any tag they like, but an administrator effectively builds a thesuarus in the background to link synonyms and prompt future users with preferred terms, and a totally controlled vocabulary.

Social search seemed to be a buzzword this year, and Gavin talked about a function that allowed you to “use people as search engines” but as far as I could tell, this actually meant the system simply recorded everybody’s search results, websites visited, etc., and then allowed other people to run a search on specific people’s collections of information. [Actually, the system runs live Google searches on the websites particular people have looked at, as well as emails, documents, etc associated with them or that are tagged a particular way - see Scott’s comment for more details].

Lewis’s talk wasn’t a cultural critique of the effect of social media on humanity, but a useful practical guide to how to avoid breaking the law and causing damage to brand reputation by using social media badly. Essentially - don’t pose as a genuine customer when you are working for a company and don’t disguise advertisements by making them indistinguishable from articles (which can be a bit of a grey area). She also suggested that it was better to have one company blog and get lots of people to contribute to it to keep it moving, than have lots of company blogs that are hardly ever updated.