Facets=Classes=Sets

Rdf-graph3
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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.

Enhanced by Zemanta~Marlene Rockmore

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