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3368Re: [TaxoCoP] data modeling and taxonomy

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  • Adrian Walker
    Jan 6, 2010
      Hi All,

      Good discussion.  Here's a hopefully useful background item.

      It's actually possible to combine data modeling, taxonomy creation and application writing in one notation -- something we call Executable English.

      Here's a simple example that adds up fund amounts over a business hierarchy:


      Apologies if you have seen this approach before, and thanks for comments.

                                                          -- Adrian
      Internet Business Logic
      A Wiki and SOA Endpoint for Executable Open Vocabulary English over SQL and RDF
      Online at www.reengineeringllc.com    Shared use is free   No advertisements

      Adrian Walker

      On Tue, Jan 5, 2010 at 10:36 PM, Patrick Lambe <plambe@...> wrote:

      Well I was just sitting back and enjoying the conversation, Bob. But since you ask, I 'll start with a comment that Matt made early on, that there might be usability issues with reusing structures from data models in taxonomies, even though in principle such reuse makes sense.

      I think there's a tendency for us to get very entity focused in these discussions and definitions and stop there. There's a good reason for this. The common ground for data models, ontologies, taxonomies is their need to establish relatively stable entities at the very least; they each do slightly different different things around the language referring to those entities, and they diverge in the type and extent of work around establishing and defining relationships and maybe inference-generating capabilities (which some taxonomy forms can support as well as ontologies). But the entities are the core point of reference.

      But Matt's comment reminds us that it's important to remember that data models, taxonomies and ontologies are at the end of the day just instruments, and to understand the instrument is not just about understanding the entities it manipulates, but how the instrument is used, and for what purpose. 

      The design of a tool is driven by its functionality, not its components. DM-T-Os serve related purposes via different means and in different contexts. There are important differences in the amount of human vs machine processing expected or served. As Matt suggests master data management is one way of getting a handle on how they can inter-operate. But fixing an entity and definition in one space (eg a data model) does not unquestionably qualify it for use in another space (eg a taxonomy).

      I think we also assume that usability is only really relevant at the taxonomy level. In my book I suggested that taxonomies are for humans and ontologies are for machines, which risks feeding that assumption. But at the end of the day, the rationale for using any of these instruments whether data models, taxonomies or ontologies, is that they must emerge into human use in some way. It's just that for DMs and Os machine processes provide different opportunities and constraints from human ones. If we can't see the pathway to human use (which is where some of the visionary talk on ontologies falls down, I feel) then they risk floating away into philosophical (or organisational) abstractions. I think this is where a lot of the hard wrestling work needs to be done, to resolve relationships between the instruments, preserve a common core where possible, and reflect the context-driven needs at organisational and user levels.

      This is all very abstract still... I think what would be useful would be some good clear cases where we can see the relationships in specific contexts.


      Patrick Lambe

      website: www.straitsknowledge.com

      Have you seen our KM Method Cards or
      Organisation Culture Cards?  

      On Jan 6, 2010, at 7:30 AM, Bob Bater wrote:

      Heather, Gabriel, John, Keith & anyone else who's following this thread:


      I'm still feeling my way around these kinds of issues (have been for years), and have no hard-and-fast solutions. However, I do have some 'working hypotheses' which I find to be helpful. I'll refer to them as I respond to a few points made by John, Keith and Gabriel.


      Firstly, John is quite right in pointing out that both data models and taxonomies are necessarily bounded. Who'd want to undertake a data model or a taxonomy of *everything*? Well, I suppose Melville Dewey, UDC, LCC have all attempted it, with varying degrees of success. But that's a topic for another day. In an organizational context, both data models and taxonomies need to be restricted to a specific domain, if only for practical reasons.


      John also says:

      > For example, if all of the 'entities' that a data modeller wanted to use were already classified by a taxonomist and resided in a master data management inventory, then a sort of symbiotic relationship could exist between the necessarily narrow application of the data and the universal 'connectivity' of a fully faceted business vocabulary. <

      I see this as the role of the 'over-arching ontology which expresses the context of both data model and taxonomy', to quote my own post. The ontology, developed first, ensures that both data modeller and taxonomist are singing from the same hymn sheet. That will also prove of great benefit to data warehouse developers, document managers, records managers and information architects, further down the line.


      Keith says that he finds taxonomies are regarded as:

      > "THE solution" rather than being viewed as "A solution" or part of a larger system of models and decision-making depending on the nature of the enterprise <

      Taxonomies have been over-egged. Many in the field think 'taxonomy' first and context later. IMHO bad! Build the ontology first, then do your data modelling. Then you'll have done a PoC (Proof of Concept) for the domain - identifying the entities which are important, their important attributes (for the data modellers) and a first lead-in to the language people use to refer to them (for the taxonomists). Using both the ontology and the data model, define the key attributes which different communities regard as important to them when they want to access and process information. That gives you a metadata application profile for each community which can be aggregated into a corporate metadata profile. Only then do you look at each attribute in each profile and decide how it is to be populated. Sometimes, it will be an /ad hoc/ value; sometimes the value will be drawn from a fixed, flat list; sometimes the value will be drawn from an organized, maintained hierarchy of values - a taxonomy. For me, the metadata profile comes first. A taxonomy only becomes relevant if a metadata element requires it.


      Gabriel said:

      > (I said  "ontology / taxonomy" in the above because I'm not clear myself whether our CM does satisfy a full definition of "ontology"; for example as yet we have no mechanisms for making inferences). <


      My 'working hypothesis' in this respect does not include the need for ontologies to enable the making of inferences. That is a requirement of strict 'ontologies' in the Semantic Web sense. For me, ontologies provide the context for ensuring that information and knowledge management structures and systems are coherent and interoperable.


      Keith said:

      > Getting at just where taxonomy, data modeling, and ontology specification begin, end, and overlap is really welcome.  <


      Again, my 'working hypothesis' is that ontologies come first, specifying the entities involved in an activity system, and their relationships. Data modellers will want to define the attributes of each entity and to characterize their relationships more rigorously, to enable their capture in the highly structured world of the DBMS, focused on logical consistency.


      Information managers, on the other hand, are less data-focused and more user-focused, concerned with linking entities and their key attributes to the concepts - and the terms which represent those concepts - employed by workers. So - where appropriate - they build a taxonomy proposing terms to be used for those concepts, reflecting the taxonomic relationships inherent in any domain - generic, partitive, instantial. While the taxonomy can establish the entities (concepts) involved, and their relationships, it cannot dictate the terms which people use to refer to those concepts. Provision is made therefore for variance in terminology by developing a thesaurus, which allows people to search using their native term, and for back-end software to translate this into the 'preferred term' established by the taxonomy.


      Hope that stimulates some thoughts. Meanwhile, where's Patrick Lambe in this thread? Patrick, I'm sure you have some informative views on these issues. Please join us.





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