Tag Archives: taxonomy

KO

Assessing information taxonomies using epistemology and the sociology of science

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I am delighted that the Journal of Documentation accepted my article about subjectivity and objectivity in taxonomy work for publication.

The article is based on the work I did for my MRes dissertation at UCL, and I am extremely grateful for the support of Vanda Broughton, everyone at the Department of Information Studies, the wonderful taxonomists and information professionals who helped me with my research, and ISKO UK.

Building bridges: Linking diverse classification schemes as part of a technology change project

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My paper about my work on the linking and migration of legacy classification schemes, taxonomies, and controlled vocabularies has been published in the Journal for Business Information Review.

KO

Building Enterprise Taxonomies – Book Review

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Estimated reading time 2–2 minutes

There aren’t many books on taxonomies, so it is good to have another on the shelf. Darin L Stewart‘s book is based on a series of lectures and provides a good introduction to key topics. As a format, that means you can pick the sections that are relevant to you. It has a very American student textbook tone, with pop quotes and definitions of key concepts in information science (e.g. precision and recall), but that doesn’t mean it isn’t a useful refresher for professionals. I particularly enjoyed the sections on XML, RDF, and ontologies as most of the coverage of these topics is either highly technical or very abstract. As the title suggests, it has a very corporate focus and so doesn’t really cover scientific taxonomies or library classifications.

The chapters introduce the concept of findability, cover the basics of metadata, types of taxonomies, how to go about developing a taxonomy and performing a content audit, general guidance on choosing terms and structures, some of the technical issues – introducing XML, XSLT, RDF, and OWL, and summarising ontologies and folksonomies.

I found a few typos and a few places slightly odd – for example I found the use of “Google whacking” to illustrate “teleporting” confusing and the descriptions of how to go about taxonomy work to be a little prescriptive. However, textbooks have to simplify the world in order to provide students with a starting point. Overall the book covers a good range of topics and concepts and is a light but informative read.

Online Information Conference – day two

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Estimated reading time 6–10 minutes

Linked Data in Libraries

I stayed in the Linked Data track for Day 2 of the Online Information Conference, very much enjoying Karen Coyle‘s presentation on metadata standards – FRBR, FRSAR, FRAD, RDA – and Sarah Bartlett‘s enthusiasm for using Linked Data to throw open bibliographic data to the world so that fascinating connections can be made. She explained that while the physical sciences have been well mapped and a number of ontologies are available, far less work has been done in the humanities. She encouraged humanities researchers to extend RDF and develop it.

In the world of literature, the potential connections are infinite and very little numerical analysis has been done by academics. For example, “intertextuality” is a key topic in literary criticism, and Linked Data that exposes the references one author makes to another can be analysed to show the patterns of influence a particular author had on others. (Google ngrams is a step in this direction, part index, part concordance.)

She stressed that libraries and librarians have a duty of care to understand, curate, and manage ontologies as part of their professional role.

Karen and Sarah’s eagerness to make the world a better place by making sure that the thoughtfully curated and well-managed bibliographic data held by libraries is made available to all was especially poignant at a time when library services in the UK are being savaged.

The Swedish Union Catalogue is another library project that has benefited from a Linked Data approach. With a concern to give users more access to and pathways into the collections, Martin Malmsten asked if APIs are enough. He stressed the popularity of just chucking the data out there in a quick and dirty form and making it as simple as possible for people to interact with it. However, he pointed out that licences need to be changed and updated, as copyright law designed for a print world is not always applicable for online content.

Martin pointed out that in a commercialised world, giving anything away seems crazy, but that allowing others to link to your data does not destroy your data. If provenance (parametadata) is kept and curated, you can distinguish between the metadata you assert about content and anything that anybody else asserts.

During the panel discussion, provenance and traceability – which the W3C is now focusing on (parametadata) – was discussed and it was noted that allowing other people to link to your data does not destroy your data, and often makes it more valuable. The question of what the “killer app” for the semantic web might be was raised, as was the question of how we might create user interfaces that allow the kinds of multiple pathway browsing that can render multiple relationships and connections comprehensible to people. This could be something a bit like topic maps – but we probably need a 13-year-old who takes all this data for granted to have a clear vision of its potential!

Tackling Linked Data Challenges

The second session of day two was missing Georgi Kobilarov of Uberblic who was caught up in the bad weather. However, the remaining speakers filled the time admirably.

Paul Nelson of Search Technologies pointed out that Google is not “free” to companies, as they pay billions in search engine optimisation (SEO) to help Google. Google is essentially providing a marketing service, and companies are paying huge amounts trying to present their data in the way that suits Google. It is therefore worth bearing in mind that Google’s algorithms are not resulting in a neutral view of available information resources, but are providing a highly commercial view of the web.

John Sheridan described using Linked Data at the National Archives to open up documentation that previously had very little easily searchable metadata. Much of the documentation in the National Archives is structured – forms, lists, directories, etc. – which present particular problems for free text searches, but are prime sources for mashing up and querying.

Taxonomies, Metadata, and Semantics: Frameworks and Approaches

There were some sensible presentations on how to use taxonomies and ontologies to improve search results in the third session.
Tom Reamy of KAPS noted the end of the “religious fervour” about folksonomy that flourished a few years ago, now that people have realised that there is no way for folksonomies to get better and they offer little help to infrequent users of a system. They are still useful as a way of getting insights into the kind of search terms that people use, and can be easier to analyse than search logs. A hybrid approach, using a lightweight faceted taxonomy over the top of folksonomic tags is proving more useful.

Taxonomies remain key in providing the structure on which autocategorisation and text analytics is based, and so having a central taxonomy team that engages in regular and active dialogue with users is vital. Understanding the “basic concepts” (i.e. Lakoff and Rosch’s “basic categories”) that are the most familiar terms to the community of users is vital for constructing a helpful taxonomy and labels should be as short and simple as possible. Labels should be chosen for their distinctiveness and expressiveness.

He also pointed out that adults and children have different learning strategies, which is worth remembering. I was also pleased to hear his clear and emphatic distinction between leisure and workplace search needs. It’s a personal bugbear of mine that people don’t realise that looking for a hairdresser in central London – where any one of a number will do – is not the same as trying to find a specific shot of a particular celebrity shortly after that controversial haircut a couple of years ago from the interview they gave about it on a chat show.

Tom highlighted four key functions for taxonomies:

  • knowledge organisation systems (for asset management)
  • labelling systems (for asset management)
  • navigation systems (for retrieval and discovery)
  • search systems (for retrieval)

He pointed out that text analytics needs taxonomy to underpin it, to base contextualisation rules on. He also stressed the importance of data quality, as data quality problems cause the majority of search project failures. People often focus on cool new features and fail to pay attention to the underlying data structures they need to put in place for effective searching.

He noted that the volumes of data and metadata that need to processed are growing at a furious rate. He highlighted Comcast as a company that is very highly advanced in the search and data management arena, managing multiple streams of data that are constantly being updated, for an audience that expects instant and accurate information.

He stated that structure will remain the key to findability for the foreseeable future. Autonomy is often hailed as doing something different to other search engines because it uses statistical methods, but at heart it still relies on structure in the data.

Richard Padley made it through the snow despite a four-hour train journey from Brighton, and spoke at length about the importance of knowledge organisation to support search. He explained the differences between controlled vocabularies, indexes, taxonomies, and ontologies and how each performs a different function.

Marianne Lykke then talked about information architecture and persuasive design. She also referred to “basic categories” as well as the need to guide people to where you want them to go via simple and clear steps.

Taxonomies, Metadata, and Semantics in Action

I spoke in the final session of the day, on metadata life cycles, asset lifecycles, parametadata, and managing data flows in complex information “ecosystems” with different “pace layers”.

Neil Blue from Biowisdom gave a fascinating and detailed overview of Biowisdom’s use of semantic technologies, in particular ontology-driven concept extraction. Biowisdom handle huge complex databases of information to do with the biological sciences and pharmaceuticals, so face very domain-specific issues, such as how to bridge the gap between “hard” scientific descriptions and “soft” descriptions of symptoms and side-effects typically given by patients.

In the final presentation of the day, Alessandro Pica outlined the use of semantic technologies by Italian News agency AGI.

KO

In the beginning was the word: the evolution of knowledge organisation

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Estimated reading time 3–5 minutes

I was delighted to be introduced by Mark Davey to Leala Abbott on Monday. Leala is a smart and accomplished digital asset management consultant from the Metropolitan Museum of Art in New York and we were discussing how difficult it is to explain what we do. I told her about how I describe “the evolution of classification” to people and she asked me to write it up here. So, this is my first blog post “by commission”.

word
In the beginning there was the word, then words (and eventually sentences).

list
Then people realised words could be very useful when they were grouped into lists (and eventually controlled vocabularies, keyword lists, tag lists, and folksonomies).

taxonomy
But then the lists started to get a bit long and unwieldy, so people broke them up into sections, or categories, and lo and behold – the first taxonomy.

faceted taxonomy
People then realised you could join related taxonomies together for richer information structuring and they made faceted taxonomies, labelling different aspects of a concept in the different facets.

ontology
Then people noticed that if you specified and defined the relationships between the facets (or terms and concepts), you could do useful things with those relationships too, which becomes especially powerful when using computers to analyse content, and so ontologies were devised.

Here is a very simple example of how these different KO systems work:

I need some fruit – I think in words – apples, pears, bananas. Already I have a shopping list and that serves its purpose as a reminder to me of things to buy (I don’t need to build a fruit ontology expressing the relationships between apples and other foodstuffs, for example).

When I get to the shop, I want to find my way around. The shop has handy signs – a big one says “Fresh fruit”, so I know which section of the shop to head for. When I get there, a smaller sign says “Apples” and even smaller ones tell me the different types of apples (Gala, Braeburn, Granny Smith…). The shop signs form a simple taxonomy, which is very useful for helping me find my way around.

When I get home, I want to know how to cook apple pie, so I get my recipe book, but I’m not sure whether to look under “Apples” or “Pies”. Luckily, the index includes Apples: Pies, Puddings and Desserts as well as Pies, Puddings and Desserts: Apples. The book’s index has used a faceted taxonomy, so I can find the recipe in either place, whichever one I look in first.

After dinner, I wonder about the history of apple pies, so I go online to a website about apples, where a lot of content about apple pies has been structured using ontologies. I then can search the site for “apple pie” and get suggestions for lots of articles related to apples and pies that I can browse through, based on the ideas that the people who built the ontology have linked together. For example, if the article date has been included, I could also ask more complex questions such as “give me all the articles on apple pies written before 1910″, and if the author’s nationality has been included, I could ask for all the articles on apple pies written before 1910 by US authors.

People often ask me if a taxonomy is better than a controlled vocabulary, or if an ontology is the best of all, but the question doesn’t make sense out of context – it really depends what you are trying to do. Ontologies are the most complex and sophisticated KO classification tools we have at the moment, but when I just want a few things from the shop, it’s a good old fashioned list every time.

Using taxonomies to support ontologies

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Estimated reading time 4–6 minutes

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.

Taxonomy as an application for an open world

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Estimated reading time 9–15 minutes

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.