Tag Archives: indexing

To index is to translate

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Estimated reading time 3–4 minutes

Living in Montreal means I am trying to improve my very limited French and in trying to communicate with my Francophone neighbours I have become aware of a process of attempting to simplify my thoughts and express them using the limited vocabulary and grammar that I have available. I only have a few nouns, fewer verbs, and a couple of conjunctions that I can use so far and so trying to talk to people is not so much a process of thinking in English and translating that into French, as considering the basic core concepts that I need to convey and finding the simplest ways of expressing relationships. So I will say something like “The sun shone. It was big. People were happy” because I can’t properly translate “We all loved the great weather today”.

This made me realise how similar this is to the process of breaking down content into key concepts for indexing. My limited vocabulary is much like the controlled vocabulary of an indexing system, forcing me to analyse and decompose my ideas into simple components and basic relationships. This means I am doing quite well at fact-based communication, but my storytelling has suffered as I have only one very simple emotional register to work with. The best I can offer is a rather laconic style with some simple metaphors: “It was like a horror movie.”

It is regularly noted that ontology work in the sciences has forged ahead of that in the humanities, and the parallel with my ability to express facts but not tell stories struck me. When I tell my simplified stories I rely on shared understanding of a broad cultural context that provides the emotional aspect – I can use the simple expression “horror movie” because the concept has rich emotional associations, connotations, and resonances for people. The concept itself is rather vague, broad, and open to interpretation, so the shared understanding is rather thin. The opposite is true of scientific concepts, which are honed into precision and a very constrained definitive shared understanding. So, I wonder how much of sense that I can express facts well is actually an illusion, and it is just that those factual concepts have few emotional resonances.

A major aspect of poetry is about extending the meanings of words to their limits, to allow for the maximum emotional resonance and personal interpretation. Perhaps poetry speaks to individuals precisely because it doesn’t evoke a shared understanding but calls out new meanings and challenges the reader to think differently, to find new meanings? This is the opposite of indexing, which is about simplifying and constraining to the point at which all the fuzziness is driven away and you are left with nothing but “dead metaphors”. The only reason indexing the sciences seems easier is because so many scientific concepts have been analyzed and defined to this point already, doing much of the indexer’s work for them.

I am not sure if these musings have any practical applications. People sometimes ask me if I think my previous studies of languages and literature have helped in my current work. I have known many excellent monolingual indexers but am also aware that many people who are good at semantics speak more than one language. However, I am sure it is helpful to think of the process of indexing as a form of translation, albeit if the idea of removing all the poetry from language in order to create a usable, useful index is not at all romantic!

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!

Tag you’re it – but is your tag the same as my tag?

Estimated reading time 5–8 minutes

Lots of people talk about tags, and they all tend to assume they mean the same thing. However, there are lots of different types of tag from HTML tags for marking up web pages to labels in databases and this can lead to all sorts of confusion and problems in projects.

Here are some definitions of “tag” that I’ve heard and that are different in significant ways. If you think my definitions can be improved, please comment, and please let me know of any other usages of that tricksy little word “tag” that you’ve happened upon.

 1) A tag is a free text keyword you add as part of the metadata of something to help search

Free text tags are usually uncontrolled and unstructured (folksonomic) simple strings of characters. Free text tagging functionaliy is usually no more than a simple text field in a database, so it very easy to implement technically. For limited collections, collections with low research value, user-generated collections, and collections that are not otherwise catalogued, free text tags provide the ability to do at least some searching (e.g. if you have a small collection of still images that have no other metadata attached, any subject keyword tags are better than none).

Folksonomic tagging was hailed as revolutionary a few years ago because it is cheap. However, it fails to solve numerous information retrieval problems. Most significantly, if you use free text tags, you need to do additional work later on to disambiguate them (apple, apple, or apple – company, record label, fruit?) or add any structure to them, including grouping synonyms to provide a more complete search (a search for “automobile” can’t retrieve items tagged “car” unless you can associate these synonyms in a synset, synonym ring, or thesaurus).

 2) A tag is a keyword that is selected from a controlled vocabulary or authority list

Controlled keywords are more useful than free text tags because they reduce the problems of synonyms and disambiguation by making the person applying the tag choose from a limited set of terms. It is easier to build a thesaurus containing all the controlled keywords, as you are not trying to encompass every possible word in the language (or indeed any string of characters that somebody might make up). Controlled vocabularies also avoid apparently trivial but practically problematic issues such as spelling variants and errors and use of abbreviations. However, flat controlled vocabularies become very unwieldy once you have more than about 50 terms. There may be a numeric identifier associated with a controlled vocabulary keyword, but it is usually only some kind of local internal system identifier.

Tags taken from controlled lists are often used for process-driven functions, as opposed to search or browse functions. So, someone might apply a tag from a controlled list to designate a workflow status of an asset. For such processes, it is usually fairly straightforward to control the vocabulary options available, so that only a few labels are available. Linguistic nuances are not so important in such contexts – people are just taught what the options are and usually it doesn’t occur to them to try to use other terms. If the available terms are inadequate, this often means there is something wrong with the business process or the system design (e.g. we need a workflow state of “pending approval” but we only have the labels “created” and “approved”).

 3) A tag is a keyword that is selected from a taxonomy

Once a controlled vocabulary becomes too long to be easy to navigate, it can be “chunked up” or “broken down” into a taxonomy.
Keywords in taxonomies are more useful than keywords in flat controlled vocabularies because the taxonomy holds information about the relationships between terms. The simplest relationship is broader>narrower (parent>child). This means you can “chunk up” your flat vocabulary list into sections, e.g. to make it easier to navigate, to offer ways a researcher can modify their search (didn’t find what you wanted – try a broader search, too many results – try a narrower search). Usually internal IDs are used to connect the label displayed in the UI with the graph that contains the relationships between the concepts.
Often a taxonomy will also hold associative (“see also”) relationships, effectively extending the taxonomy to be a taxonomy-with-thesaurus.

 4) A tag is a type of Uniform Resource Identifier (URI)

This is the Linked Open Data approach. There are important differences between tag URIs and other types of tag. URI tags have to conform to various technical conventions and standards that support interoperability. In Linked Open Data contexts, URI tags are usually public and shared, rather than being private IDs. Relationships between URIs are usually expressed in an ontology, rather than a taxonomy (although the ontology may associate taxonomies or the ontology may be derived from pre-existing taxonomies).

 5) A tag is metadata added to a web page for search engines to index

It is possible to add any of the above types of tag to a web page (you can say a web page is just another type of asset). Differences between tags on assets and tags on web pages are usually to do with the ways those tags are stored and how they are used by other systems (i.e. a stock management system will need different information to a search engine). Search engine optimisation (SEO) bad practices led to a decline in the use of keyword tagging for search engine indexing, although the Semantic Web returns to the principle that content creators are the best people to index their content (see next section).

For web pages, the tags are often added in the header information, along with other instructions to the browser. On indiviudal assets (e.g. photos, videos) in content or asset management systems, the tags are often held in a particular field in a database. For Linked Open Data systems (whether managing web pages, traditional assets, or combinations of both), the tag URIs and their relationships (triples) are usually stored in a triple store, rather than conventional database.

With web pages, tagging can become very complex, as there might be a mixture of URI tags and basic labels, and a web page can be a complex information system in its own right, containing sub-elements such as audio and video content that itself might have various tags.

 6) A tag is a label used to mark up content within a web page that can be used for display purposes and for indexing

The language that is used to write web pages (HTML) is often described as comprising tags. So, you tag up flat text with instructions that tell the browser “this is a heading”, “this is a paragraph” etc. With the advent of HTML5 and vocabularies such as schema.org, more and more semantic information is being included in these tags. Search engines can use this information, for example to create more specific indexes.

So, when you ask someone if the content is tagged, and they say yes, it is always worth checking you both actually mean the same thing!

People better than algorithms – official!

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< 1 minute

Google Invests in Pixazza, An AdSense for Images is another neat little crowdsourcing initiative. What interested me the most was this: “As James Everingham, Pixazza’s CTO, states, “No computer algorithm can identify a black pair of Jimmy Choo boots from the 2009 fall collection in the same way a person can. Rather than rely on image analysis algorithms, our platform enlists product experts to drive the process.” ”

In other words, they are paying indexers/cataloguers. Not very much, it’s true, but it is still good to see someone in tech admitting that old fashioned human beings still have their uses! Image recognition algorithms are getting better all the time, but we haven’t even really conquered text processing yet.


New method for building multilingual ontologies

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< 1 minute

New method for building multilingual ontologies appeared on AlphaGalileo.Org – the Internet-based news centre for European science, engineering and technology. Researchers at the Universidad Politécnica de Madrid’s School of Computing (FIUPM) claim to have created a language-independent ontology-building tool. I think it will work very well for consistent well-structured information – for example in catalogues and directories – but it seems to me that it is essentially being an “auto-indexer” that only really works if you control linguistic forms, and perhaps even vocabulary, very tightly. That’s great – and means plenty of work for editors making sure everything is neat, tidy, and consistent to suit the system – but isn’t it going to be an awful lot of work? Or am I massively missing the point?

Reuters Wants the World To Be Tagged

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Estimated reading time 1–2 minutes

Reuters Wants The World To Be Tagged. This article on the ReadWrite Web blog is about the new API (does anyone else pronounce this “appy”?) sent out into the world by Reuters. They are hoping it will encourage tagging of articles in a way they can then harvest. It sounds like it is fairly basic at the moment – it is only recognising a few bits and pieces like people and places. It would be interesting to see how well it does with people like Jack London and places like Congo (Brazzaville) and Congo (Kinshasa). When I worked on a similar project we had lots of problems disambiguating the Guineas (Papua New, Equatorial, etc) and Salvadors (El or San) in particular. I assume they have lots of authority files backed up by rules that will sort all those out. It would be nice to see “under the bonnet” as it were!