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.

    Enhanced by Zemanta
    Advertisements

    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