There are classifying and clustering tools that help you along, but don’t produce ontologies. They essentially filter the noise, much like free text search engines filter and then rank documents according to some relevance ranking algorithm. That is, they can produce candidates based on text analysis, and those candidates are mostly node-based, i.e., names of potential categories: classes mainly; properties: maybe but these are harder; relations: not much either. These technologies are term-based, i.e., based mostly on statistical NLP and text analysis. Both ontology learning and ontology induction from text are very primitive, as of now. Their results can be input into an ontology-adjudication phase, but are just early tools. If you want axiomatic ontologies, forget it. Thanks, Leo From: ontology-summit-bounces@xxxxxxxxxxxxxxxx [mailto:ontology-summit-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Michael F Uschold
Sent: Wednesday, March 02, 2011 5:22 PM
To: arun; Ontology Summit 2011 discussion
Subject: Re: [ontology-summit] An example of the worth of ontology development There are lots and lots of tools and products that do this. I am not familiar with them myself. Google "ontology learning". Michael On Wed, Mar 2, 2011 at 12:32 PM, Arun <arun@xxxxxxxxxxxx> wrote: Does anyone have any tools that go from raw data to produce Ontologies
as output or even proto-ontologies as outputs that humans can then edit
and refine?
On 3/2/11 12:50 PM, Jack Ring wrote:
> Quite so.
> That's why helping them understand their enterprise as a system, hopefully an intelligent system, gives them the perspective to grok the strange distinctions that ontologists need to make.
> This starts with a little semantic modeling; then activity modeling of the problematic situation (customers, markets, competitors, etc., and Their customers, markets, competitors, etc.,); then formulating an intervention strategy for serving unmet, even unrecognized, market needs better than can competitors and rivals; then design/architecture of To Be enterprise; then
> teasing out the infrastructure and modularization. All this must precede the development on ontology (because ontology is a major facet of infrastructure).
>
> Ways of accomplishing intelligent enterprise systems architecting and engineering are being evovled.
>
> Unfortunately the NOISE created by Business Process Management, Knowledge Management, Business Rules management, Enterprise Architecture Frameworks (for paint-by-numbers, i.e., ignorant, enterprises), etc., is precluding rapid development of this capability.
>
> Meanwhile, there are already places that have recognized the need for intelligent infrastructure. These are the current market targets for ontology insertion. In general, it is any enterprise or market wherein He Who Learns Fastest Wins. Specific examples are Military Intelligence, Business Intelligence, Conference Management (evolving to social network interlocutor), Learning Management (as modern education of youth is finally freed from government intervention), and Autonomous System engagement management. Personalized, Molecular-level medicine may become the Killer App.
>
> Make sense?
>
> On Mar 2, 2011, at 10:10 AM, John F. Sowa wrote:
>
>> Jack and Mike,
>>
>> I agree with that point, but I'd like to add some qualifications:
>>
>> JR
>>> The primary purpose of a semantic model is to facilitate knowledge
>>> exchange and choice making in a gaggle of humans in hopes of
>>> morphing the gaggle into a system. A key usage is to inform the
>>> development of an executable ontology, e.g., application software,
>>> for automation of information flow and decision. Another key purpose
>>> is to provide a basis for objective assessment of enterprise
>>> situation (aka evidence-based management).
>> MU
>>> Yes, this is the kind of thing I'm after.
>> The primary qualification is that the "gaggle of humans" can only
>> agree on what they understand. The people who work in a field
>> can all agree that a list of familiar words, as documented in
>> their familiar texts, cover their familiar subject matter.
>>
>> But when ontologists start to axiomatize those terms in some
>> arcane notation based on some arcane distinctions about
>> endurants, perdurants, continuants, etc., all bets are off.
>>
>> John
>>
>>
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