Tag Archives: tagging

Making KO Work: integrating taxonomies into technology

Lincoln Cathedral
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Estimated reading time 6–10 minutes

The recent ISKO UK event Making KO Work: integrating taxonomies into technology offered four very different but complementary talks, followed by a panel session. These provided a good overview of current practice and largely concluded that although technology has advanced, there is still need for human intervention in KO work.

Can You Really Implement Taxonomies in Native SharePoint?

Marc Stephenson from Metataxis gave a clear and helpful overview of the key terms and principles you need to know when using taxonomies and folksonomies in SharePoint. SharePoint is very widely used as an enterprise document repository, and although its taxonomy management capabilities are limited, when combined with an external taxonomy management solution, it can enable very effective metadata capture.

The first step is to become familiar with the specialised terminology that SharePoint uses. Metadata in SharePoint is held as “Columns”, which can be System Columns that are fixed and integral to SharePoint functionality, or Custom Columns, which can be changed and which need to be managed by an information architecture role. For example, Columns can be set as “Mandatory” to ensure users fill them in. Columns can be configured to provide picklists or lookups, as well as being free text, and can be specified as “numeric”, “date” etc. Taxonomies can be included as “Managed Metadata”.

Different “Content Types” can be defined, for example to apply standardised headers and footers to documents, enforce workflow, or apply a retention/disposal policy, and many different pre-defined Content Types are available. Taxonomies are referred to as “Managed Term Sets”, and these can be controlled by a taxonomist role. “Managed Keywords” are essentially folksonomic tags, but SharePoint allows these to be transferred into Managed Term Sets, enabling a taxonomist to choose folksonomic tags to become part of more formal taxonomies.

The “Term Store Manager” provides some functionality for taxonomy management, such as adding synonyms (“Other Labels”), or deprecating terms so that they can no longer be found by users when tagging (but remain available for search). Terms can also be deleted, but that should only be done if there is a process for re-tagging documents, because a deleted tag will generate a metadata error the next time someone tries to save the document. Limited polyhierarchy is possible, because the same term can exist in more than one “Managed Term Set”.

“Term Groups” can be defined, which can be useful if different departments want to manage their own taxonomies.

There are various limitations – such as a maximum number of Managed Terms in a Term Set (30,000) and if SharePoint is deployed online across a large organisation, changes can take some time to propagate throughout the system. The process of importing taxonomies needs to be managed carefully, as there is no way to re-import or over-write Term Sets (you would end up with duplicate sets) and there is no easy way to export taxonomies. There is no provision for term history or scope notes, and no analytics, so SharePoint lacks full taxonomy management functionality.

There are companion taxonomy management products (e.g. SmartLogic’s Semaphore, or Concept Searching) and it is possible to use other taxonomy management tools (such as PoolParty, Synaptica, or MultiTes) but an additional import/export process would need to be built.

So, SharePoint offers a lot of options for metadata management, but is better as a taxonomy deployment tool than a master taxonomy management tool.

Integrating Taxonomy with Easy, Semantic Authoring

Joe Pairman of Mekon Ltd, demonstrated a very user-friendly lightweight set of tagging tools that allow non-expert users the ability to add rich metadata to content as they work. This addresses a key problem for taxonomists – how to ensure subject matter experts or authors who are more focused on content than metadata are able to tag consistently, quickly, and easily. By taking a form-based approach to content creation, authors are able to add structural metadata as they work, and add tags to specific words with a couple of clicks. This is particularly effective with a pre-defined controlled vocabulary.

The example Joe showed us was a very clear commercial use case of Linked Data, because the controlled vocabulary was very specific – products for sale. Each product was associated with a DBPedia concept, which provided the URI, and where a match to the text was detected the relevant word was highlighted. The user could then click on that word, see the suggested DBPedia concept, and click to tag. The tool (using FontoXML and Congility technology) then applied the relevant RDF to the underlying XML document “behind the scenes”, in a process of “inline semantic enrichment”. This approach enables accurate, author-mediated tagging at a very granular level. The customers reading the content online could then click on the hghlighted text and the relevant products could be displayed with an “add to cart” function, with the aim of increasing sales. As an added bonus, the tags are also available for search engines, helping surface very accurately relevant content in search results. (Schema.org tags could also be included.)

Enhancement of User Journeys with SOLR at Historic England

Richard Worthington of Historic England described the problems they had when deploying a SOLR/Lucene search to their documents without any taxonomy or thesaurus support for searching. They soon found that SQL searches were too blunt an instrument to provide useful results – for example, searching for “Grant” at first would bring up the page about the grants that were offered, but as soon as they added more data sets, this frequently searched-for page became buried under references to Grantchester, Grantham, etc.

Although they could manage relevancy to a certain extent at the data set level and by selecting “top results” for specific searches, the search team realised that this would be a painstaking and rigid process. It would also not address the problem that many terms used by the subject matter expert authors were not the same as the terms general users were searching for. For example, general users would search for “Lincoln Cathedral” rather than “Cathedral Church of St Mary of Lincoln”. So, they have much work for human taxonomists and thesaurus editors to do.

Applied Taxonomy Frameworks: Your Mileage May Vary

Alan Flett of SmartLogic took us through the latest enhancements to their products, showcasing a new feature called “Fact Extraction”. This works by identifying the context around specific data and information, in order to drive Business Intelligence and Analytics. The tool is essentially a user-friendly simplified algorithm builder that allows very specific searches to be constructed using pre-defined “building blocks”, such as “Facts”, “Entities”, and “Skips”. This means a specific piece of information, words to ignore, and entities such as a number or a date can be specified to construct a complex search query. This allows the search results to be defined by context and returned in context, and is especially effective for well-structured data sets. It also means that results are framed in a standardized format, which is useful for analytics.

Concluding Panel

Although techniques such as automated classification, machine learning, and AI are progressing all the time, these still work best when combined with a well-structured knowledge base. Creating that knowledge base relies on human intelligence, especially for the familiar problems of disambiguation and synonym collection, in particular where the content authors have a different approach or level of domain expertise to the end users of the search systems. The panel agreed that for both the creation of thesauruses, taxonomies, and ontologies and for the deployment of these in tagging, semi-automated approaches remain necessary, and so there is still much to be done by human taxonomists, ontologists, and information architects in order to make knowledge organisation work.

Image: Lincoln Cathedral. Photo by Zaphad1

Tagging the cart before the horse – Getting your project plan in order

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Estimated reading time 6–9 minutes

When people launch search improvement or information organziation projects, one of the commonest mistakes is to be over-eager to “just get the content indexed or tagged” without spending enough time and thought on the structure of an index, what should be tagged, and how the tags themselves should be structured.

This typically happens for two reasons:
1. The project managers – often encouraged by service providers who just want to get their hands on the cheque – simply underestimate the amount of preparatory work involved, whether it is structuring and testing a taxonomy, setting up and checking automated concept extaction rules, or developing a comprehensive domain model and tag set, so they fail to include enough – if any – of a development and testing stage in the plan. This often happens when the project is led by people who do not work closely with the content itself. Projects led by marekting or IT departments often fall into this trap.

2. The project managers include development and testing, with iterative correction and improvement phases, but are put under pressure to cut corners, or to compress deadlines.  This tends to happen when external forces affect timescales – for example local government projects that have to spend the budget before the end of the financial year. It can also happen when stakeholder power is unevenly distributed – for example, the advice of information professionals is sought but then over-ruled by more powerful stakeholders who have a fixed deadline in mind – for example a launching a new website in time for the Christmas market.

Forewarned is forearmed

Prevention is better than cure in both these scenarios, but easier said than done. Your best defence is to understand organizational culture, politics, and history and to evangelize the role and importance of information work and your department. Find out which departments have initiated information projects in the past, which have the biggest budgets, which have the most proactive leadership teams, then actively seek allies in those departments. Find out if there are meetings on information issues you could attend, offer to help, or even do something like conduct a survey on information use and needs and ask for volunteers to be interviewed.  Simply by talking to people at any level in those departments you will start to find out what is going on, and you will remind people in those departments of your existence and areas of expertise.

On a more formal level, you can look at organizational structures and hierarchies and make sure that you have effective chains of communication that follow chains of command. This may mean supporting your boss in promoting the work of your department to their boss. This is especially important in organizations with lots of layers of middle management, as middle managers can get so caught up in day to day work that longer term strategy can get put on the back burner, so offer support.

If you find out about projects early enough, you have a chance of influencing the project planning stages to make sure information and content issues are given the attention they need, right from the start.

Shutting the stable door…

Sometimes despite our best efforts we end up in a project that is already tripping over itself. A common scenario is for tagging work to be presented as a fait accompli. This is particularly likely with fully automated tagging work, as processing can be done far faster than any manual tagging effort. However, it is highly unusual for any project to be undertaken without its being intended to offer some sort or service or solve some recognized problem.

Firstly, assess how well it achieves its intended goals. If you have only been called into the project at the late stage, is this because it is going off the rails and the team want a salvage solution, or is it because it works well in one context and the team want to see if it can be used more widely? If it is the latter, that’s great – you can enjoy coming up with lots of positive and creative proposals. However, the core business planning principles are pretty much the same whether you are proposing to extend a successful project or corralling one that is running out of control.

Once you know what the project was meant to achieve, assess how much budget and time you have left, as that will determine the scope to make changes and improvements. Work out what sort of changes are feasible. Can you get an additional set of tags applied for example? Can you get sets of tags deleted? Are you only able to make manual adjustments or can you re-run automated processes? How labour intensive are the adjustment processes? Is chronology a factor – in other words can you keep the first run for legacy content but evolve the processes for future content?

These assessments are especially valuable for projects that are at an intermediate stage as there is much more scope to alter their direction. In these cases it is vital to prioritize and focus on what can be changed in a pragmatic way. For example, if the team are working chronologically through a set of documents, you may have time to undertake planning and assessment work focused on the most recent and have that ready before they get to a logical break point. So, you prioritize developing a schema relevant to the current year, and make a clean break on a logical date, such as January 1. If they have been working topic by topic, is there a new search facet you could introduce and get a really good set for that run as a fresh iteration?

If there are no clean breakpoints or clear sets of changes to be made, focus on anything that is likely to cause user problems or confusion or serious information management problems in future. What are likely to cause real pain points? What are the worst of those?

Once you have identified the worst issues and clarified the resources you have for making the changes, you have the basis for working up the time and money you need to carry them out. This can form the basis of your business case and project plan either to improve a faltering project and pull it back on track or to add scope to a project that is going well.

…after the horse has bolted

If there is limited scope to make changes, and the project is presented as already complete, it is still worth assessing how well it meets its goals as this will help you work out how you can best use and present the work that has been done. For example, can it be offered as an “optional extra” to existing search systems?

It is also worth assessing the costs and resource involved in order to make changes you would recommend even if it seems there is no immediate prospect of getting that work done. It is likely that sooner or later someone will want to re-visit the work, especially if it is not meeting its goals. Then it will be useful to know whether it can be fixed with a small injection of resource or whether it requires a major re-working, or even abandoning and starting afresh. Such a prospect may seem daunting, but if you can learn lessons and avoid repeating mistakes the next time around, then that can be seen as a positive. If one of the problems with the project was the lack of input from the information team early on, then it is worth making sure for the sake of the information department and the organization as a whole that the same mistake does not happen again. If you demonstrate well enough how you would have done things differently, you might even get to be in charge next time!

Libraries, Media, and the Semantic Web meetup at the BBC

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Estimated reading time 3–4 minutes

In a bit of a blog cleanup, I discovered this post languishing unpublished. The event took place earlier this year but the videos of the presentations are still well worth watching. It was an excellent session with short but highly informative talks by some of the smartest people currently working in the semantic web arena. The Videos of the event are available on You Tube.


Jon Voss of Historypin was a true “information altruist”, describing libraries as a “radical idea”. The concept that people should be able to get information for free at the point of access, paid for by general taxation, has huge political implications. (Many of our libraries were funded by Victorian philanthropists who realised that an educated workforce was a more productive workforce, something that appears to have been largely forgotten today.) Historypin is seeking to build a new library, based on personal collections of content and metadata – a “memory-sharing” project. Jon eloquently explained how the Semantic Web reflects the principles of the first librarians in that it seeks ways to encourage people to open up and share knowledge as widely as possible.


Adrian Stevenson of MIMAS described various projects including Archives Hub, an excellent project helping archives, and in particular small archives that don’t have much funding, to share content and catalogues.


Evan Sandhaus of the New York Times explained the IPTC’s rNews – a news markup standard that should help search engines and search analytics tools to index news content more effectively.


Dan Brickley’s “compare and contrast” of Universal Decimal Classification with schema.org was wonderful and he reminded technologists that it very easy to forget that librarians and classification theorists were attempting to solve search problems far in advance of the invention of computers. He showed an example of “search log analysis” from 1912, queries sent to the Belgian international bibliographic service – an early “semantic question answering service”. The “search terms” were fascinating and not so very different to the sort of things you’d expect people to be asking today. He also gave an excellent overview of Lonclass the BBC Archive’s largest classification scheme, which is based on UDC.

BBC Olympics online

Silver Oliver described how BBC Future Media is pioneering semantic technologies and using the Olympic Games to showcase this work on a huge and fast-paced scale. By using semantic techniques, dynamic rich websites can be built and kept up to the minute, even once results start to pour in.

World Service audio archives

Yves Raimond talked about a BBC Research & Development project to automatically index World Service audio archives. The World Service, having been a separate organisation to the core BBC, has not traditionally been part of the main BBC Archive, and most of its content has little or no useful metadata. Nevertheless, the content itself is highly valuable, so anything that can be done to preserve it and make it accessible is a benefit. The audio files were processed through speech-to-text software, and then automated indexing applied to generate suggested tags. The accuracy rate is about 70% so human help is needed to sort out the good tags from the bad (and occasionally offensive!) tags, but thsi is still a lot easier than tagging everything from scratch.

Are you a semantic romantic?

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Estimated reading time 8–12 minutes

The “semantic web” is an expression that has been used for long enough now that I for one feel I ought to know what it means, but it is hard to know where to start when so much about it is presented in “techspeak”. I am trying to understand it all in my own non-technical terms, so this post is aimed at “semantic wannabes” rather than “semantic aficionados”. It suggests some ways of starting to think about the semantic web and linked open data without worrying about the technicalities.

At a very basic level, the semantic web is something that information professionals have been doing for years. We know about using common formats so that information can be exchanged electronically, from SGML, HTML, and then XML. In the 90s, publishers used “field codes” to identify subject areas so that articles could be held in databases and re-used in multiple publications. In the library world, metadata standards like MARC and Dublin Core were devised to make it easier to share cataloguing data. The semantic web essentially just extends these principles.

So, why all the hype?

There is money to be made and lost on semantic web projects, and investors always want to try to predict the future so they can back winning horses. The recent Pew Report (thanks to Brendan for the link) shows the huge variety of opinions about what the semantic web will become.

On the one extreme, the semantic evangelists are hoping that we can create a highly sophisticated system that can make sense of our content by itself, with the familiar arguments that this will free humans from mundane tasks so that we can do more interesting things, be better informed and connected, and build a better and more intelligent world. They describe systems that “know” that when you book a holiday you need to get from your house to the airport, that you must remember to reschedule an appointment you made for that week, and that you need to send off your passport tomorrow to renew it in time. This is helpful and can seem spookily clever, but is no more mysterious than making sure my holiday booking system is connected to my diary. There are all sorts of commercial applications of such “convenience data management” and lots of ethical implications about privacy and data security too, but we have had these debates many times in the past.

A more business-focused example might be that a search engine will “realise” that when you search for “orange” you mean the mobile phone company, because it “knows” you are a market analyst working in telecoms. It will then work out that documents that contain the words “orange” and “fruit” are unlikely to be what you are after, and so won’t return them in search results. You will also be able to construct more complex questions, for example to query databases containing information on tantalum deposits and compare them with information about civil conflicts, to advise you on whether the price of mobile phone manufacture is likely to increase over the next five years.

Again, this sort of thing can sound almost magical, but is basically just compiling and comparing data from different data sets. This is familiar ground. The key difference is that for semantically tagged datasets much of the processing can be automated, so data crunching exercises that were simply too time-consuming to be worthwhile in the past become possible. The evangelists can make the semantic web project sound overwhelmingly revolutionary and utopian, especially when people start talking in sci-fi sounding phrases like “extended cognition” and “distributed intelligence”, but essentially this is the familiar territory of structuring content, adding metadata, and connecting databases. We have made the cost-benefit arguments for good quality metadata and efficient metadata management many times.

On the other extreme, the semantic web detractors claim that there is no point bothering with standardised metadata, because it is too difficult politically and practically to get people to co-operate and use common standards. In terms familiar to information professionals, you can’t get enough people to add enough good quality metadata to make the system work. Clay Shirky in “Ontology is overrated” argued that there is no point in trying to get commonalty up front, it is just too expensive (there are no “tag police” to tidy up), you just have to let people tag randomly and then try to work out what they meant afterwards. This is a great way of harvesting cheap metadata, but doesn’t help if you need to be sure that you are getting a sensible answer to a question. It only takes one person to have mistagged something, and your dataset is polluted and your complex query will generate false results. Shirky himself declares that he is talking about the web as a whole, which is fun to think about, but how many of us (apart from Google) are actually engaged in trying to sort out the entire web? Most of us just want to sort out our own little corner.

I expect the semantic web to follow all other standardisation projects. There will always be a huge “non-semantic” web that will contain vast quantities of potentially useful information that can’t be accessed by semantic web systems, but that is no different from the situation today where there are huge amounts of content that can’t be found by search engines (the “invisible web” or “dark web”) – from proprietary databases to personal collections in unusual formats. No system has been able to include everything. No archive contains every jotting scrawled on a serviette, no bookshop stocks every photocopied fanzine, no telephone directory lists every phone number in existence. However, they contain enough to be useful for most people most of the time. No standard provides a perfect universal lingua franca, but common languages increase the number of people you can talk to easily. The adoption of XML is not universal, but for everyone who has “opted in” there are commercial benefits. Not everybody uses pdf files, but for many people they have saved hours of time previously spent converting and re-styling documents.

So, should I join in?

What you really need to ask is not “What is the future of the semantic web?” but “Is it worth my while joining in right now?”. How to answer that question depends on your particular context and circumstances. It is much easier to try to think about a project, product, or set of services that is relevant to you than to worry about what everyone else is doing. If you can build a product quickly and cheaply using what is available now, it doesn’t really matter whether the semantic web succeeds in its current form or gets superseded by something else later.

I have made a start by asking myself very basic questions like:

  • What sort of content/data do we have?
  • How much is there?
  • What format is it in at the moment?
  • What proportion of that would we like to share (is it all public domain, do we have some that is commercially sensitive, but some that isn’t, are there data protection or rights restrictions)?

If you have a lot of data in well-structured and open formats (e.g. XML), there is a good chance it will be fairly straightforward to link your own data sets to each other, and link your data to external data. If there are commercial and legal reasons why the data can’t be made public, it may still be worth using semantic web principles, but you might be limited to working with a small data set of your own that you can keep within a “walled garden” – whether or not this is a good idea is another story for another post.

A more creative approach is to ask questions like:

  • What content/data services are we seeking to provide?
  • Who are our key customers/consumers/clients and what could we offer them that we don’t offer now?
  • What new products or services would they like to see?
  • What other sources of information do they access (users usually have good suggestions for connections that wouldn’t occur to us)?

Some more concrete questions would be ones like:

  • What information could be presented on a map?
  • How can marketing data be connected to web usage statistics?
  • Where could we usefully add legacy content to new webpages?

It is also worth investigating what others are already providing:

  • What content/data out there is accessible? (e.g. recently released UK government data)
  • Could any of it work with our content/data?
  • Whose data would it be really interesting to have access to?
  • Who are we already working with who might be willing to share data (even if we aren’t sure yet what sort of joint products/projects we could devise)?

It’s not as scary as it seems

Don’t be put off by talk about RDF, OWL, and SPARQL, how to construct an ontology, and whether or not you need a triple store. The first questions to ask are familiar ones like who you would like to work with, what could you create if you could get your hands on their content, and what new creations might arise if you let them share yours? Once you can see the semantic web in terms of specific projects that make sense for your organisation, you can call on the technical teams to work out the details. What I have found is that the technical teams are desperate to get their hands on high quality structured content – our content – and are more than happy to sort out the practicalities. As content creators and custodians, we are the ones that understand our content and how it works, so we are the ones who ought to be seizing the initiative and starting to be imaginative about what we can create if we link our data.

A bit of further reading:
Linked Data.org
Linked Data is Blooming: Why You Should Care
What can Data.gov.uk do for me?

Why bother with information architecture? — RenaissanceCMS

Estimated reading time 1–2 minutes

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”.

Managing the Crowd: Records Management 2.0

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Estimated reading time 1–2 minutes

I’ve just read Steve Bailey‘s book Managing the Crowd: Records Management 2.0. It is a thought-provoking and timely read and very enjoyable as well. There’s an RM 2.0 Ning site too. There’s a good summary on the TFPL website. Bailey is clear that he is trying to provoke debate, so I will raise a question. There is a widely held belief that people like tagging, but I’m not sure that this applies once you get into the office. People love to tag their own photos on Flickr, but is that because they like tagging or because they like their own photos? Similarly, people like to tag their own blog posts, but is this not a rather self-selecting sample? If you have the time, motivation, and energy to blog, the additional burden of adding a few tags to try to get yourself a few more readers is hardly great. So, is there any evidence out there that people tag work documents just as enthusiastically as they tag stuff about themselves? Are they really as enthusiastic about thinking of appropriate tags for financial reports and product information sheets as they are about tagging their favourite songs or You Tube clips?


Vocab Control

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Estimated reading time 2–2 minutes

Having spent years working as an editor fussing over consistency of style and orthography, I shouldn’t have been as surprised as I was to find my tags on even this little blog site, written solely by me, had already become a mess. It didn’t take too long to tidy them up, but there are only a handful of articles here so far.

I worked with some extremely clever people in my first “proper” job back in the 90s, and we used to have a “90%” rule regarding algorithmic-based language processing (we mostly processed very well-structured text). However brilliant your program, you’d always have 10% of nonsense left over at the end that you needed to sort out by hand – mainly due to the vagaries of natural language and general human inconsistency. I’m no expert on natural language processing, but I get the impression that a lot of people still think 90% is really rather good. Certainly auto-classification software seems to run at a much lower success rate, even after manual training. It strikes me that there’s a parallel between folksonomies and this sort of software. Both process a lot of information on cheaply, so make possible processing on a scale that just couldn’t be done before, but you still need someone to tidy up around the edges if you want top quality.

I think the future of folksonomies depends on how this tidying-up process develops. There are various things happening to improve quality – like auto-complete predictive text. Google’s tag game is another approach, and ravelry.com use gentle human “shepherding” of taggers, personally suggesting tags and orthography (thanks to Elizabeth for pointing this one out to me).

I would really like to get hold of some percentages. If 75% is a decent showing for off-the peg auto-categorisation/classification software, and we could get up to 90% with bespoke algorithms processing structured text, what perecentages can you expect from a folksonomic approach?