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.
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.