What next, taxonomy?

Can anyone learn what is happening in a field by following a conference on Twitter? Tha’s how I decided to follow Taxonomy Bootcamp 2012.   I missed networking with a new generation of confident, well-trained taxonomists, but nevertheless, I will attempt to identify the themes and challenges that are facing taxonomists.  Please feel free to add to the list or dispute!

  1. Centralized models don’t scale; think federated, allow local variation.

Taxonomists have jobs because organizations value managing its resources with a common vocabulary, but how the architecture and governance of the vocabulary maps to the internal structure of an organization  is harder to understand.  Speakers urged attendees to allow distributed models that accommodate local variations and utilize local project vocabularies.  Using term sets (facets) help ease taxonomy managemement. As an example,  one tweet posted about the talk given by Gary Carlson and Pam Green, relayed that Microsoft had 23 term sets for its intranet – the most complex was products, and the simplest was confidentiality.  This structure assists in setting up access control to owenrship  groups manage these vocabularies at the group and/or term level.

2. Does social media and information sharing have a role in taxonomy and how does it square with governance, security, control, and confidentiality

This topic yielded some buzz and offers an interesting dialetic between the issue of information access and security. @syndetic tweeted that “HR sees social media as a time waster, and IT sees it as a security probem.”     So let’s start with exploring to see why  social media matters to taxonomists? The main point is that information is ambigouous and confusing, and that social media is necessary to help generate, identify and clarify relevant, lively, topical content now that can be curated later (as opposed to traditional models of curate then share).  AprilMunden  summed the argument for social media nicely in a tweet: “using folksonomy (user generated taxonomy) should defInitely be a part of your initial design AND ongoing governance plan.”   Seth Earley, always with  a witty insight,  said “Fast changing, requires expertise, ambiguous concepts …business can own; slow to change, more security,unambiguous,  let IT own that”     Maybe that’s a strategy for starting the conversation about the business value of tagging and social media.

3.  Why non-expert taxonomist needs to build a taxonomy.

“Experts swim in the deep end of the content.” Tom Reamy made this statement which set off a flurry of sympathetic tweets.  Tom clarified that experts know their content and process but can get mired in details and in their point-of view   Tom’s statement is provocative because it clarifies the role of the taxonomist which is

  • to be apply social  listening skills and analytic methods to categorize diverse needs of a wide range of users
  •  Build a well-structured faceted taxonomy that reflects needs of different constituencies and fits with the governance structure of an organization (see point 1)
  •  Validate and test the taxonomy
  • Assist with the support of application integration
  • Establish ongoing governance processes including use of social media and text analytic to keep the taxonomy relevant

A taxonomy needs to reflect multiple user needs which means allowing non-experts to explore topics before they go in for a deep dive.  Taxonomists need to work with experts to gain their support by engaging and in validating the taxonomy.

3. What aboutTaxonomy, the User Interface, and Big Data and Mobile apps

Taxonomists sit at an intersection between  data/digital assets/content and user interfaces, but it’s not clear how taxonomists apply their skills.  They are not graphic designers or UX experts, and not quite database experts.  A few sessions mentioned data visualization, and other graphic imagery to explore content and data such as   mashups of datasets,  grids, wheels, graphic organizers, or maps. This is an area where taxonomists, who are not as visually oriented, need to rethink their approach, to start to think of themselves as information artists.  Taxonomists can be advocates for adopting in improved techniques including standards that organize taxonomy/metadata framework  and can also advocate for tools that make sharing between across applications, organization and platforms more efficient, which brings us to point 4

4. Taxonomy tools must make it easier to import and export vocabularies

Taxonomists know that their vocabularies need to play well with all the applications above as well as other needs such as the goal of providing cross-organization information access Sharepoint, XML, relational databases, legacy products.   Taxonomies work in part because they are agnostic, because they can work in with any number of technologies, because concepts and metadata are separate from the content.  To play well with others, taxonomy tools  need to support import and export of vocabularies into different standards including SKOS or XML,  As KarlaTR tweeted ”If you put something in a taxonomy, you’ve got to get it out.”     One option is to try tools that are marketed at Taxonomy Bootcamp such as TopQuadrant EVN and Smartlogic which have been ported from the ontology world are now alongside Synaptica and Information Access as part of the tool evaluation process.

So what next, taxonomy?     What is nice to hear is that more taxonomists are surviving because their organizations understand their core roles. What’s the emerging topics  and challenges —  how to distribute and decentralize (localize)  while having authority and control, how to collect new content on emerging, current topics, visualization, how to be more agile, how to fit in with new technologies like social media, mobile, and big data.  Phew!  That’s a challenge.  Taxonomists have a chance to build relationships not only between terms, but with stakeholders in on a the way to a compelling, visualized, multidimensional content strategy.  Good luck.

Deconfusing Healthcare through Taxonomy Inquiry

This winter,  I had an opportunity to participate in an information research team that had a chance to interview top executives in health care in Massachusetts.  This included the CEOs of insurance companies,  regulators from the Attorney General’s office, and medical directors of major medical networks and hospitals.   The goal of this project was to understand one term  “Cost Containment”   — what are the drivers for rising health care costs and what can be done to slow the rate of growth.

When someone with taxonomy skills participates in these types of investigations, it is hard not to put those taxonomy skills to work. What did I learn from this process that might be applicable to best practice and to understanding health care cost containment?

1) Start with a  simple but important question  as a guide for developing deeper knowledge

This group started with the question  “What is cost containment?”   It is a fairly fundamental question since we in Massachusetts are fortunate to have universal coverage (about 97%)  but there is a need to control costs.  By asking this fundamental question. the group could  collect basic facts from each key player on the same topic   to understand how proposed strategies are defined from the point of view of key players who are shaping policy.

2) Get to know the cast of characters

Remember the adage that the key to a baseball game is to know the players and the same applies to understanding a complex issue. We need to  who the users are, what brought them to these meetings,  It is critical to  identify the constituencies in healthcare, all of whom have different goals in any situation.   The key actors we indentified were:

  • Insurers (also known as Payers)
  • Providers (Hospitals, Doctors, Specialists)
  • Regulators (government, legislature, attorney general)
  • Consumers (includes business owners, patients, local government)
  • Purchasing agents (people who buy insurance for large groups — government, business, insurance agents)
The above list is a top level of the Actors/Player facet which further breakdowns.  Insurers for example is further categorized into companies, corporate structure (profit/non-profit), market share.    Not all the groups under these broad headings share characteristics.  For examples, we rarely saw a “specialist” at  a meeting on cost containment, but other types of medical personnel including primary care, psychiatrists, behavior medicine, were well represented because they, as a group, lower reimbursement and higher volume than specialists.  Grouping does not mean all values are inherited  — thus the need for understanding power relationships and attributes.

3) Understand the power relationships

Some actors have more power and are core to the discussion.  Insurers and providers have a closer affinity for example, while consumers, including employees,  business and local government entities tend to have less to no power in these relationships.  Hospitals and specialists have more power than primary care and behavioral medicine.  Understanding these internecine wars within health care is a key analysis for understanding core relationships and who is outlying.  The health care debate is in part about how to give outliers more power and equity in the health care process. The most outlying of all voices is patients and consumers.  Theoretically,  in new models of health care, their voice is supposed to be represented by larger purchasing pools who can negotiate for better service at less cost.

4) Identify  the key cost drivers —  Isolate the attributes 

The hardest part of this work is to isolate the variables/attributes  or cost drivers, and understand how each group contributes to improving these practices.  These are topics that should be of mutual concern but that are  not universally understood and standardized.  Examples of cost drivers included:

  • Use of and dissemination of best practices (end-of-life care, chronic diseases)
  • Use of Technology
  • Number  and Variety of Insurance Plans
  • Cost of drugs
  • Reimbursement rates
  • Risk Management (use of defensive medicine, malpractice, high-risk pools)

Each of these attributes needed to be further understood from perspective of the key players to understand how it contributes to cost.  For example, Massachusetts has an excellent universal health care law, where consumers can choose from about 18 different plans over the Connector, but in addition, there are additional public, private and individual plans resulting in over 16,000 different plans.   Some cost containment could be achieved by having a “shared minimal contract” that is at a high standard of care, and captures essence of basic wellness.  To do this, the players and consumers need to find the common language for describing conditions and coverage.

5) Capture the AS IS Definitions.

Since these conditions and coverage are not standardized,  it is useful to understand what the current status is.   Understanding AS IS definitions help to capture the many disconnects between group. For example, while consumers argue about cost of deductibles, insurance companies might spend more money in order to reduce high cost of hospitalization.  Result is like a balloon filled with water — one end gets leaner, while more pressure is put on another end of the balloon — the consumer.    Capturing the cacophony, instead of the symphony, turned out to be the most valuable part of the work. We discovered we did not have to reach common understanding, which meant trying to capture the current status and its impacts.

6) Read background content

In addition to understand the “cast and drivers”  it is also important to read studies and literature to keep a broad and balance perspective. Being in rooms with charming and knowledgeable power players can be quite intoxicating, but to keep it honest, we needed to keep reading and we needed to ask honest questions about what was the advantage for each player in their advocacy for a certain program.   Spending a few hours each week on literature reviews, books, articles, podcasts on general health care was very important to building our group and individual knowledge base and developing our facility in the terminology of health care economics.  We used reading to define comparative health care models in other countries (Taiwan, Switzerland, Japan, Canada, Germany, UK, France, and US) and to understand multiple models of healthcare delivery.

7) Capture concepts in simple diagrams

Even within our small, random  data collection group, there were divisions in understanding can be quite diverse.  Using simple diagrams to capture concepts  turned out to be powerful shared way to come to common understanding.  Bubble mapping, graphing, hierarchical diagrams, any visual graph was useful to clarify information.

8)  If any term is hard to explain with a simple sentence, it probably deserves a taxonomy

“Cost containment”  is not trivial,  but it is also important to understand. And it is almost  impossible to explain without learning something about healthcare system.   It is worthy of the time and effort to create a taxonomy to define the information space or information void, and a void is filled by misunderstanding or misinformation.

Developing a consumer-focussed taxonomy for navigating health care  turns out to be valuable work, but it is hard to sustain without a dedicated team with and sustained funding.  A consumer-focused taxonomy would help  navigate the health care debate, can be used across all actors, including   insurers, providers, governmental entities  and consumers who want to share information with a confused but curious public.

~ Marlene Rockmore

External User-facing Search and Taxonomies

Search has become faster, cheaper and more intelligent since the days of inverted single word engines so why not just use a search engine.  Why bother with taxonomy? Let’s briefly revisit what search is suppose to do. A search engine needs to make a pretty good guess of what a user wants to find – an unspoken intent which is expressed in staccato keywords  and then search needs to  match the user query with some content (documents, data, digital objects, people with expertise, adserving, product information), and take some action such as  read, buy, forward, share, comment, browse…..Sometimes the match is exact, sometimes the query terms are a partial match and sometimes there is no match.

In other words, search is not a perfect art.  The units of this equation are not just search  engine.  It is also the quality of the content and the query.   Good search needs good content,  no matter how great the technology.

Someone responsible for search implementation  has limited control over two of the key ingredients of search – the technology and the content.  This is why taxonomy plays a role – it can help describe concepts not in the content or in the metadata about the content.  (metadata is particularly useful for digitizing non-digital objects).   Taxonomy is not always necessary –  If you can write custom content with very precise vocabulary using Search Engine Optimization (SEO) techniques might not need a taxonomy. But documents cannot be altered, such as emails or reports, where it would be a significant protocol violation, even illegal.  When is search not enough?

1)  Developing effective measures to assess when search is not enough – the 80/20 rule

As part of the some of the early work in faceted taxonomies I did, I spent some time at MIT working on a research project that compared results when we queried a system that was based on a search engine technology alone,  and when we queried one where the query could be enhanced by adding taxonomy terms. For this experiment, we had the advantage of using a system, that was the brainchild of Wendi Pohs,  in which we had 2 search engines  using the same technology processing similar documents that were made available to a user interface which had a simple search box like Google. One engine processed news feeds .  These feeds were added quickly with no intervention—directly loaded into a search engine.   What our research found was that search engines without a taxonomy, left unattended, flatlined. The recall never improved over 75-80%.   Lee Romero, who is a keen observer of search, has recently done an excellent blog post observing this same flatline phenomenon.

What to do when you want to do better than 80% and move the flatline

In the same experiment, we created second engine, using the same software, had a taxonomy function where we inserted taxonomy terms into the index. These terms were selected from query logs and analytic reports-  they were unmatched terms, misspellings,  abbreviations.   There was an added cost to add taxonomy terms, but there was no impact on speed or performance of search since search technology used the same engine.

The taxonomy  was divided into classes such as product, company, or subject. Each term was connected to another term by using user-defined cross- connections (associative terms) which was smart enough to infer other relevant terms.  At least one of these terms  in the linked sets had to be tagged by an indexer.  So,  if a product was tagged, then we could infer that the product was <made_by> a company, thus speeding the tagging process.  Taggers could override the automated suggestions, and/or add new rules, by the way.  This way we could ensure exhaustive indexing at a low cost and effort.  The taxonomy-controlled section  paid back this effort.  A search on this section  would recall content that matched user-query terms about 90% of the time.  The taxonomy-controlled part of the database  could be improved.  We also worked hard to acquire content- good content- in many formats that would improve the quality of the database and thus what goes into an engine.

By using reports, tools and measurements, we were able to proactively add equivalents and monitor emerging terms.    Dips in performance triggered action to understand what was changing in the user’s world – was it query terms, a search for emerging content, or other unmet needs.

Errors were due to 1) missing content 2) wrong application 3)new terms or spelling errors that could be quickly added to taxonomy and 4) new and emerging trends that users were identifying that had not yet been captured in the taxonomy – all issues that could be identified and corrected.For example,  in the recent flu season,  search engines would eventually learn that  H1N1 was the preferred term to Swine Flu,  but in some cases, it was much easier for a trained taxonomy editor to surgically make this connection (especially in a fast moving news and business cycle). In a search engine only scenario, these errors are not always identifiable not actionable.

Set realistic goals and explanations for what taxonomy can do

ROI discussion often mean conversations that start or end with “Taxonomy can increase sales by improving conversion”  or “lower costs.”   Here are a few reasons that might be more honest and even compellng

Help with Ambiguity and add Precision —  Use Faceted Navigation: Search engines have a hard time differentiating about very key concepts and terms.    I remember in the early days when a term like “ASK” would bring a search engine to its knees because it couldn’t tell the difference between the name of  system command or a computer company.   By sorting terms into facets, we could help differentiate and resolve ambiguity by navigating user to the right facet and by tagging more precisely.   A developer looking for information on Java applications shouldn’t be sent to Java the island.  Taxonomy can help keep users searching down paths that might lead to results that are useful.  That’s productivity.

Implement Universal Search: A taxonomy can be implemented independently from the content, which means it can be used across content types- blogs, videos, email – creating a common set of concepts from which to generate user-centered search.   That’s efficiency and smart use of limited resources.  You need to have common metadata or rdf to take advantage of universal search, but there are standards such as Dublin Core that can help jumpstart that conversation.

Think Scalability and Reuse: Taxonomy can be used across applications, which means a central, faceted taxonomy, can be reused by other applications.  The best practice however is to create smaller taxonomies that are divided into homogenous facets.   To design monolithic spaghetti-like taxonomies will, in the end, create more work, bad inferences, and sour you on the whole project.  Reuse and scalability avoid redundant efforts.  Cost savings.

Use Taxonomies to Manage Change: Since taxonomy is independent of the content, you can change the concepts in the taxonomy without impacting the content.   Taxonomies are NOT static. For example,  many organizations need to change organizational names.  These names can be subsumed in a taxonomy without impacting the existing content. It’s safer and more secure way to handle change.

Create a technical and cost plan to integrate taxonomy while maintaining speed and performance, and not adding to overhead costs.

Implementing taxonomy within search can be done at various price points —  a solution like Vivismo  is not within every budget but there are other options low cost  and effective alternatives  I’ve found include:   Here are some technical considerations in adding taxonomy.

  • You don’t need a high end faceted navigation tool to get benefits of faceted navigation. Faceted navigation allows a user to narrow or broaden or expand query at time of search. This can be done in many CMS systems including  Drupal.   WordPress, which is what I use for this blog, has a taxonomy module, allows multiple authors
  • Add custom fields or metadata  for tagging that could be loaded into the search engine to improve search (as SOLR does)
  • If you have the budget and requirement for high-thoughput as in  auto-classification and text analytics, as in nStein, Teragram or Vivismo, then taxonomy is  still very useful to improve precision of results and making collections within document sets.

The bottom line is that whether you use search engine, you should be confident that 80% of the time, the user will get what they want. If you need to find ways to improve the user experience, taxonomy is one highly viable, low-cost and effective option.  Taxonomy might be worth looking as a way to give a  insert a pacemaker into the heart of  a search engine that seems to have flatlined.

Once you have a backbone with classy taxonomies and metadata, you can then proceed to the creative activity of beautiful designs of navigation paths for your end users.  But keep your eye  For more on search and taxonomies, see also my prior book review of Peter Morville’s  Search Patterns.

~ Marlene Rockmore

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Image via Wikipedia

I just returned from an intense training in semantic web technologies through Top Quadrant and I learned much more about what goes on “under the covers.” The course explained more about how semantic technologies can generate machine to machine applications. One important learning was that facets are similar to classes which is similar to the mathematical idea of a set and discusses why taxonomists and programmers need to think more in terms of classes, facets and sets as similar ideas.

Using semantic tools requires building a conceptual model — which is collection of classes.  To build useful models that are semantically-enable requires learning the basic semantic toolkit:

  • RDF (relational description framework). In RDF, one creates classes, and designs relations between individual members of a class and between classes. RDF comes in two main flavors:  RDFa which is for web-based applications  and RDFs which can be used to generate the ontology (concept mapping) as a schema to represent the underlying data.  RDF is used to create inverted graphs that can be converted to triples. Using RDF, one can read in a data store such as a spreadsheet and quickly generate a starter taxonomy (which still needs to be validated with use case scenarios )
  • SKOS (simple knowledge organization system) converts traditional taxonomies into rdf format. SKOS handles basic thesaurus-type relations such as broader/narrower concepts, alternative labels and related concepts. In SKOS the related concept would have its own unique resource identifier. SKOS can only describe a concept with broader, narrower and alternative labels and preferred labels, and cannot associate a concept with an OWL class.
  • SPARQL is a specialized query language, designed to query triple stores A semantically-enabled applications is one that is converted can be converted into an RDF graph, which can then be visually displayed as a graph and queried using SparQL.
  • OWL (web ontology language) is the underlying language for describing models. OWL is required to handle more complexity such as restrictions, cardinality, and inferencing.

Most everything conceptually in RDF, SKOS, and the underlying programming language OWL, once you get under the covers, will familiar to taxonomists. Some details can confuse you, but don’t let the lack of underlying naming conventions deter you. For example, a class in RDF is called an Owl:Thing. If a class is defined in RDF Schema language can be called an RDFS:Class. Oh well, confusing, but don’t let that deter you from appreciating the power of this approach. A thing is still a class, which is similar to a facet.

Here are some examples of how OWL and taxonomies are similar. The bolded print is the OWL property.

SubClassOf defines narrow term in a set

Inverse of creates reciprocal relations

Transitivity allows navigation of a hierarchy so that if A = B, B=C, the A=C. A SPARQL query that can chain through a hierarchy can potentially consist of 2 lines.

Restrictions are similar to slot facets or attributes which are o properties that limit the set

Here are some reasons to utilize classes in semantic technologies as a best practice.  Without implementing classes and modeling, these outcomes would be hard to achieve:

Form follows function: Instead of designing big monolithic hierarchical taxonomies, thinking in terms of classes or facets, which are groupings of individual members in a set. These smaller, faster sets (fasets, perhaps) will be easier to export, import, edit and share. Perhaps facets should be called fast sets or fasets! Plus the facets (classes) can become fields in a web form. The possibilities for reuse and design opens many options.

Scalability and Reuse: Since concepts and the associated classes are independent of data and content, the concepts and classes can be changed, such as changing an organization name, renaming key terms, or adapting new ideas, without changing underlying queries and systems architecture. This is scalable.

Change Schema Without Changing Content: Developing conceptual mapping can be done independently and designed and changed in the RDF schema or OWL language without changing the underlying data. Precision: Because an individual concept can be easily manipulated as a member of a set, or multiple sets, the concept can have a more accurate definition. For example, take a term like “Chevy Chase.” By associating “Chevy Chase” with a class:Person one can distinguish Chevy Chase, the comedian, from Chevy Chase, Maryland as part of the class: Location. Furthermore, ideally each unique concept of Chevy Chase would have its own namespace or unique resource identifier (URI).

Precision: The ability to create a concept independent of the content without tightly coupling into a hierarchy, but allowing the concept to associate in a clear way with the appropriate facet or class and to get more precision. This same logic can be applied to more amorphous, squishy terms like “Compensation” or “Performance” or “Management” or “Quality” which can be deconstructed into more specific variants like “Executive Compensation” vs “Non-exempt Pay and Benefits” RDFs can be used to link to more appropriate term with a unique URI

Facilitate Linked Data: If taxonomies and data can be shared, it is faster to build serious applications that can solve real and acute problems. In our class, we built applications that mapped free wifi hot spots were next to swimming pools and taquerias in geographic location, but we also did a serious social policy application where we mapped cities in the United States that had increases in complaints about housing due to sexual orientation, national origin, race and other discriminatory practices, taking data from multiple, reputable sources and applying a common conceptual model.

There are some new challenges for taxonomists especially in understanding the importance of inferencing. Developers who work with OWL is that many inferencing errors can be traced back to bad, messy taxonomies where there are too many broad terms — in other words, avoid complex polyhierarchies.

To create taxonomies that are ready for the semantic future, the better practice is to how to arrange concepts into facets (which can be equated with classes or sets and avoiding complex polyhierarchies (a concept with too many parents). This will allow taxonomies to play well with applications such as user interface design and machine readable applications. The first step is to stop thinking about taxonomies as a monolithic hierarchy, but rather to look at taxonomies as a collection of classes (or facets), where a class is a set with individual members. If models and taxonomies can be easily built and used to connect across data worksheets resolving issues, applications based on linked data can be quickly built.

To try  semantic tools such as SKOS editors, download a trial copy of Top Braid Composer Free Edition.

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Skills of a Classy Taxonomist

At SemTech in June 2010,  several speakers including Professor Deb McGuiness drew a very clear line was drawn between what a taxonomist does and what an ontologist does.  Taxonomists build hierarchies, and ontologists determine classes or categories.   In other words, ontologies are neat and unambiguous, and taxonomies are a bit messy.

Defining classes or ontology work  typically precedes building the taxonomy.  Defining the classes is like writing a specification for the taxonomy; in fact defining classes is the same as defining facets.   The goal of a taxonomist and ontologies should be to define a specific, unambiguous description of a term that helps manage how we find and organize content so the pathways are clear and specific; adding an ontology ensures that the term is placed in the most specific categories to help ensure clarity and lack of ambiguity. I would argue that no taxonomy is useful unless it is faceted – that is, has been divided into classes. Taxonomies work best when they share homogenous properties, and when they are smaller and focused.

By using class analysis, or facet analysis,  several problems are solved:

1)       Clarify specific terms by situation or functions: If I am interested in Java as a programming language, I want to see material related to Java as software, not as slang for coffee or  an island in the South Pacific.  If I am looking for “drill bits,”  it might be important to understand if the drill bits are for my home electric screwdriver  or for an oil rig.   Classes capture these distinctions, and help to create precise specific tagging and information retrieval.

2)       Ease longterm  maintenance issues: Christine Connors points to a simple but common example where taxonomies are built where people’s names are included as narrow terms under the role such as “Hillary Clinton” is “Secretary of State”  or “Charles Windsor” is the “Prince of Wales.” The problem is that when people filling these roles change, there is a maintenance headache.   A classy taxonomy recognizes that there is a separate class for <people> as an entity, as distinguished from <role>.  <People> and <Role>  can be connected by a predicate such as <isA>.  These distinctions are necessary for fast-changing information (such as who is dating whom in an entertainment application) or (who owns whom in a business application).

Abstraction <person> <has> <role>

Instance: Hillary Clinton <is>  Secretary of State

3)    Facilitate sharing  and importing taxonomies: Having taxonomies that are specified by a class description means the taxonomy will be more homogenous, have shared properties, and be more focused.  This will make it easier to import with less cleanup and review.  It will facilitate the use of SKOS for example. Messy taxonomies are harder to merge.

Anyone working with semantic technologies will tell you that most problems in inference happen when hierarchies in source taxonomies create odd associations by inserting a narrow or broad term. A taxonomist needs to be attentive to inferences in order to prevent false statements.   Professor Deb McGuiness calls this issue “truth maintenance.”

To keep these categories clear and distinct, ontologists rely on building a conceptual model or a picture of the domain (see earlier post on Taxonomies and modeling.)   Modeling strategies involve skills of most taxonomists.  Most taxonomists have been taught how to capture vocabulary and how to identify facets.  Check out the blog post Taxonomies and Modelling for more information.

Elaine Kendall  of Sandpiper Software, which is a concept-modelling tool.  suggested that “one could build an ontology in 2 hours.”   With new generation of tools that can create RDF/OWL from data and content,  this statement might be true.

    With good modelling tools that automatically generate RDF/OWL,such as TopQuadrant,  taxonomists might  be able to slide into the needed role as ontologists.  Taxonomists need to understand  some basic concepts in RDF/OWL to extend their skills such as what is a class, what is a property and what is a slot facet, what is class inheritance, what is meant by reciprocation and inverse properties and how to write a SPARQL query.  But more importantly,  a classy taxonomist can help become a facilitator to help build bridges between user and development communities and  to help diagnose and prevent technical problems.

    A taxonomist who is trained in ontologies  should bring the following skills:

    • Ability to create processes to identify the requirements for each class,
    • Develop  metrics to assess good results
    • Identify what vocabularies are needed and use skills to evaluate existing vocabularies, import and adapt these vocabularies to the current needs
    • Ensure the integrity and focus of vocabularies particularly when sourced from an outside vendor,
    • Develop processes to keep vocabularies current, and understand how to use metrics to “measure and improve” any vocabularies.
    • To be part of the development team to help identify if a source vocabulary might be part of false inference.

    The taxonomist works with different user communities as well as developers and helps bridge the gap between what users and experts know and what is needed to build a useful application.   A classy taxonomist has a well-rounded set of skills that can work with development teams and user organizations to build intelligent systems.

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    Is GoodRelations a Game Changer?

    One  ontology  worth watching might be GoodRelations, which is being implemented by   Best Buy.      The central component of this architecture was an ontology called GoodRelations developed by Martin Hepp, who presented at SemTech in San Francisco last week via Skype from Munich, Germany.    GoodRelations is a retail ontology which uses RDFa from XHTML webpages to populate global ontology.   But why would a major retailer use this  architecture?

    Best Buy discovered that it was impossible to be the top dog  in search engine optimization (SEO)  in every search category for every product.  To do this, they needed to have finely tuned individual pages.  They also wanted to provide immediate content about “open box” – returned items at local stores.    looking for a solution that could add more granularity, precision and localization, but still enable global search and have metadata that was controlled by the enterprise.

    GoodRelations is a retail ontology, which offers facets or classes, metadata descriptions and attributes  that are common in the retail industry.   It is expressed in RDFa which is a flavor of RDF that works in web browsers.  Yahoo Search Monkey supports RDFa,  Facebook directed graphs will support RDF.  Google snippets also support RDFa.

    Because there is common metadata, it is easy for employees or customers (who are called “user agents” in the semantic world) to tag content via templates which populate the RDF.  RDFa can be maintained in a corporate or enterprise repository which can be configured as needed for distribution in the enterprise.

    In the GoodRelations RDF, the additional metadata might include price, color, dimensions, model and other attributes that interest consumers.  GoodRelations is an ontology that can be shared over any retail enterprise in any country.  The cost per webpage, once implemented, is minimal because “user agents” are familiar with how to complete forms over the web. The RDFa can then be appended to an HTML page written in XHTML or HTML5.  These HTML code for adding the specific metadata attributes is about 30-50 lines.  This creates HTML that has more granularity than a typical <keyword> metatag. The high costs are in the metadata management.

    Adding RDFa as metadata to a webpage should be easy to adopt because it works in the current web paradigm.   Google is offering RDFa markup language that can be appended to a webpage called Google Rich Snippets.  Snippets is competing with the another format called Microformat.  The problem is that every domain needs a shared set of s metadata attributes to enable search across smaller domains.   Google is rolling out examples of RDFa for restaurants, currently only has 2500 markup pages. To see an example of snippets,  try a search on Google for “Baked Ziti.”  Drupal 7 also offers RDF, and has been implemented in http://www.whitehouse.gov, as part of the Obama Administration transparency initiative.

    Why does this interest me as a  classy taxonomist (future ontologist)?  Clearly, this technology has evolved to a point of adoption, but further adoption depends on political and organizational work to get other applications to take the risk to try RDFa.    RDFa depends on common adoption of similar metadata  This requires political and organization skills to define and manage common metadata knowledge models.  First, taxonomists understand vocabulary and metadata as a way to capture common knowledge and shared metadata.  Second, if this innovation becomes more widely adopted and gains traction,  there may be interest in building similar process in other applications in making any information that has to be shared.

    Further, if RDFa coupled with ontology and metadata management, makes data management and querying easier through SPARQL,  then more attention can be paid to the political and organizational work of working with local agencies to contribute good data and content.

    There is a long way to go to make this vision a reality.. browsers have to adopt RDFa, applications have to prove the viability and ontologies in other domains need to be created.  But in the long run, this might be a more democratic way to extend information access on the web.

    However,  to move toward this vision, faceted navigation and defining common metadata and taxonomies is  good intermediate step.  By creating faceted taxonomies and browsing, and collecting data, user communities are moving towards understanding what search fields, common language, and unambiguous terms that matter to their users.  A little semantics goes a long way.

    ~Marlene Rockmore

    First Aid for the Accidental Taxonomy

    Many successful information systems utilized taxomies and metadata, but finding taxonomists to support this work usually happens by accident. Taxonomy design and development is a specialized skill – maybe even a talent. A large organization may employ information architects, SharePoint architects, content managers, and corporate librarians, but these people most likely lack strong taxonomy experience. Although the closest matching formal education for taxonomy work is a masters in library and information science, many corporate librarians specialize in research (such as in business intelligence) and may only know about classification and organization of information based on a past library school course taken. Information architects’ experience with taxonomies may be limited to small taxonomies that fit within the limits of menu labels.

    When an organization decides it needs an enterprise taxonomy or needs to leverage and redesign existing taxonomies, then any of these aforementioned types of employees are often pressed into working as taxonomists, without prior experience.   This is what I refer to as the “accidental taxonomist,” as in the title of my recent book (see Heather Hedden, The Accidental Taxonomist, Information Today Inc., 2010 ).

    While reading my book is a good idea for anyone who becomes an accidental taxonomist, a book alone cannot teach all the needed skills. Designing and building taxonomies is a process that is fraught with decision-making. If a taxonomist or taxonomy manager is not a defined position within an organization, the “accidental taxonomists” who temporarily assume this role, no matter how skilled, still have their regular jobs to do and may not be able to devote the needed time for the taxonomy.

    Starting with a good taxonomy foundation will make it easier to maintain the taxonomy. It will save money, time and resources to get some outside help, especially during the initial stage of taxonomy development, which requires the greatest investment of hours.

    How can a taxonomy consultant help?

    • How should terms be assigned to facets
    • Should hierarchies be more deep or more broad
    • Is a complex hierarchy needed or will simpler arrangements work
    • Should taxonomy term labels be complex or simple
    • What governance is needed for longterm management of the taxonomy
    • Who should be on the governance team, and what training is needed

    Related to different levels of experience there is also a distinction between explicit knowledge, which may be explained in a book, and tacit knowledge, which is gained through expertise and is more difficult to explain or document. Taxonomists are trained to follow the industry standard guidelines, such as ANSI/NISO Z39.19-2005 Guidelines for the Construction, Format, and Management of Monolingual Controlled Vocabularies. But these are just “guidelines,” and in practical applications the guidelines may need to be modified slightly, such as when there are significant restraints on the taxonomy design. Knowing where and when it is appropriate to bend the rules and when it is not, is a part of tacit knowledge. Having the right knowledge, however, does not necessarily mean the taxonomy work gets done.

    Even within the narrower area of taxonomy expertise, it often helps to discuss and work out issues among multiple people who have an understanding of taxonomies Taxonomy work in a large organization can be a team effort. It requires different skills and perspectives to serve all its goals. In addition to the lead taxonomist with an information science background, other people needed include information architects and user experience professionals to ensure that the taxonomy fits well into the user interface and is easy to use, subject matter experts as authorities on the terminology, and IT professionals for the technical implementation of the taxonomy.

    If you are the sole taxonomist in your organization, you may want to consult with other, outside taxonomists, such as through online discussion groups, to bounce your ideas off them and get additional feedback based on their varied expertise. It’s hard to work alone without support.

    If you are at SLA Annual Conference in New Orleans on June 16, come hear my talk : “Taxonomy Made Easy: An Introduction to Taxonomies for the Accidental Taxonomist.” SLA members are mostly corporate librarians, who are likely candidates to become accidental taxonomists. I’ll help you develop your own taxonomy skills and also identify where you might need to talk to your management about consulting with others skilled in taxonomies. First aid for the accidental taxonomist is always available!

    ~ Submitted by Heather Hedden

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