3371Re: [TaxoCoP] data modeling and taxonomy
- Jan 6, 2010Hi Lisa,
While there are some accepted standards for ontology modeling practice (RDFS/OWL), there are multiple knowledge representation languages which can be used to express any 'ontology'.
Yes, indeed. Here's a quote from Ed Barkmeyer of NIST:
'What makes written knowledge an "ontology" is that the language has a grammar and an interpretation of the grammatical constructs that is suitable for automated reasoning. If most of the desired reasoning depends on your interpretations of constructs you introduced, that can't happen unless you build the engine.'
Cheers, -- Adrian
Internet Business Logic
A Wiki and SOA Endpoint for Executable English over SQL and RDF
Online at www.reengineeringllc.com Shared use is free No advertisements
ReengineeringOn Wed, Jan 6, 2010 at 11:19 AM, lisa colvin <lisacolvin@...> wrote:
Thanks for the lively discussion. It's exciting to see these ideas coming together.
While there are some accepted standards for ontology modeling practice (RDFS/OWL), there are multiple knowledge representation languages which can be used to express any 'ontology'. Typically the more expressive the language, the more expensive it is computationally. So, you need to pick the representation language which best fits your needs. If you're not building a model to drive some sort of expert system or related capabilities, a simpler knowledge representation scheme is probably better.
However, one reason people use ontology languages in general is when there is a need for strong semantics which define the relationships/ context. Even if you don't want to build an expert/recommendation/QA/NL-based system, you can still use a more formal ontology language as just a pure specification language.
So, is a faceted classification scheme an ontology? Some would say 'yes, if it uses an ontology language to express it'. Others might say it's not if you're not expressing/defining any inheritance relations. Overall, it probably doesn't matter what you call it as long as the semantics are rich enough to solve whatever problem you needed solving.
There are fundamental differences to how the various disciplines approach information modeling. What I've found most helpful in working with people in another discipline is to be very explicit on how basic terms (like "term" :) , "class", "instance", "inference") are used in expressing the model that you're sharing. The idea of "inference", for example, can vary widely between an expert system developer and an OO developer. If these terms aren't described explicitly and used consistently, people get confused.
I also found that defining the capabilities and mathematical relationship distinctions between "controlled vocabulary list", "synonym rings/synsets", taxonomy", "thesaurus", "ontology", "desciption logics",etc. is really only interesting to taxonomists/ontologists and other curious people like us. :)
:) LisaOn Tue, Jan 5, 2010 at 7:36 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.POn Jan 6, 2010, at 7:30 AM, Bob Bater wrote:
- << Previous post in topic Next post in topic >>