A lot of general knowledge would be needed in such an ontology.
For example, if the system had the information that pineapples grew at
the top of stems growing vertically out of the ground and that such
stems were disjoint from trees, then it could reject as inconsistent
with the knowledge base any statement that pineapples were found
growing on trees anywhere. (01)
If the KB also included the information that many false documents are
published dated April 1, it should flag as questionable any claim to
scientific discoveries made on that date. (02)
Predicates for temperature ranges for growth, flowering, and fruiting of
plant species should be in an ontology dealing with climate change. (03)
Do you have a link to your current ontology? (04)
-- doug foxvog (05)
On Mon, April 1, 2013 13:14, Duane Nickull wrote:
> I have been working with a climate group for a while on creating a top
> level
> domain ontology for climate tracking. While we thought we had everything
> settled, some surprises came up. In particular, we found some new
> evidence
> that lead us to believe we had missed a major portion of our model for our
> ontology pertaining to new proof global warming was having concrete
> effects. (06)
> To illustrate the point, consider this evidence:
> http://technoracle.blogspot.ca/2013/04/finally-concrete-proof-of-global-warm
> ing.html (07)
> Any ideas on how to dynamically adjust the ontology work to compensate for
> stuff like this?
>
> Duane Nickull
>
> ***********************************
> Technoracle Advanced Systems Inc.
> Consulting and Contracting; Proven Results!
> i. Neo4J, PDF, Java, LiveCycle ES, Flex, AIR, CQ5 & Mobile
> b. http://technoracle.blogspot.com
> t. @duanenickull
>
>
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>
> From: Michel Dumontier <michel.dumontier@xxxxxxxxx>
> Reply-To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
> Date: Monday, 1 April, 2013 9:12 AM
> To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
> Subject: Re: [ontolog-forum] Why a data model does not an ontology make
>
>
>
>
> On Sun, Mar 31, 2013 at 9:55 AM, John F Sowa <sowa@xxxxxxxxxxx> wrote:
>> Michel, Leo, and Michael,
>>
>> JFS
>>> > But what was produced [by the SW] failed to address the requirements
>>> Tim
>>> > proposed and many others (including Robert and me) believe are
>>> essential.
>>
>> MD
>>> > can you list/summarize the requirements and why you think the steps
>>> that
>>> > the semantic web effort has made do *not* contribute to those
>>> requirements?
>>
>> As I've said repeatedly, three words that Tim B-L emphasized in the DAML
>> proposal of 2000 were diversity, heterogeneity, and interoperability.
>>
>> In the final DAML report of 2005, two of them (diversity and
>> interoperability) were mentioned just once and heterogeneity was
>> never mentioned at all.
>>
>> I also believe that Robert Meersman's short summary is very good:
>>
>> http://starlab.vub.ac.be/website/files/MeersmanBuffaloAug2007.pdf
>>> > Why "the" Semantic Web has failed.
>>> > * Data models vs. ontologies
>>> > * Legacy systems
>>> > * Scalability
>>> > * Methodology
>>
>> For point #1, RDF + SPARQL is just YADM -- Yet Another Data Model.
>> It has few advantages and many disadvantages over data models that have
>> been in use for decades. I have no objection to YADM if people find it
>> useful, but I have serious objections to edicting any single data model
>> as a requirement for the Semantic Web.
>
> So, what we have here is at least some effort towards standardizing at
> least
> one KR format with web standards in mind. RDF specifies URIs for naming
> and
> provides an XML serialization for processing compatibility. Still, other
> serializations are available (my preferred is n-triples), and other
> vocabularies can build on it (e.g. formal KR languages like OWL or
> vocabularies for representation of specific knowledge e.g. SKOS, PROV,
> etc).
> Now that we have RDF(S) + OWL (+OWL profiles), we see many efforts to
> align
> KR languages (e.g. RIF, others) against these - which I think is highly
> desired for interoperability.
>
>
>>
>> For point #2, Tim B-L noted the importance of interoperability with
>> legacy systems, but the DAML report ignored them completely. I can't
>> blame them for not doing everything in five years, but they have not
>> done *anything* to support legacy systems in the past 13 years.
>>
>> And please do not repeat the claim that they provided a tool to convert
>> RDBs to RDF. Interoperability means that the legacy systems work with
>> the new tools *concurrently* -- not by means of forced conversion.
>>
>
> ok, how about ontop developed by Mariano Rodriguez and colleagues:
> http://ontop.inf.unibz.it/
> it enables one to map OWL-QL ontologies to SQL database and answer
> queries.
> no conversion required.
>
>
>
>> For point #3, the SW people claim that OWL is decidable. That only
>> means that decisions terminate in *finite* time -- even though that
>> time might be greater than the age of the universe. For anything
>> the size of the WWW, scalability means no worse than (N log N) time.
>>
>
> and there have been many projects to deal with scalability. Consider
> WebPie
> (http://www.few.vu.nl/~jui200/webpie.html) by Frank van Harmelan and
> colleagues which does RDFS + OWL Horst reasoning using map reduce.
>
>
>> For point #4, please reread Robert M's slides for an example of what
>> a methodology can and should support.
>>
>
> then my point is that the onus is on those that wish to bring new
> technology
> to the masses to go through the standardization effort where it can be
> subject to criticism and compromise for real world deployment.
>
> Best,
>
> m.
>
>
>
>> Leo
>>> > The closest that relational databases get to having a semantic model
>>> > is the conceptual schema, which is a type of conceptual model
>>> (modeled
>>> > in a graphic Entity-Relation-Attribute language, with cardinality
>>> restrictions).
>>
>> Unfortunately, there was never a standard for a conceptual schema, and
>> the vendors merely pasted the term 'conceptual schema' on top of what
>> they were doing anyway. They turned it into an advertising slogan.
>>
>> E-R-A + cardinality is a requirement that must be specified in any
>> conceptual schema (or ontology), but it's far from sufficient.
>> And most of the published OWL ontologies do little or nothing
>> to go beyond that level.
>>
>> From 1978 to 2000, the published R & D on the conceptual schema and
>> related issues went far beyond what the vendors provided, Tim B-L
>> cited some of that work, but the DAML developers ignored it.
>>
>> Leo
>>> > Now the above view does have rare exceptions in the database world:
>>> e.g.,
>>> > Matthew West's work immediately springs to mind. Similarly, HighFleet
>>> > (formerly Ontology Works) tries to bridge the ontology-database
>>> connection.
>>> > Also, of course deductive databases try to combine logic programming
>>> +
>>> > relational constructs, though these just focus on the
>>> implementational
>>> > apparatus you would need for more expressive ontologies, but say
>>> nothing
>>> > in particular about ontologies.
>>
>> I agree that the systems you mention are good. But there were many
>> years of very good systems that the SW ignored. Deductive DBs were
>> proposed in the 1970s -- note Planner and Microplanner. RDBs combined
>> with Prolog and other AI tools have been widely used since the '80s.
>> Tim B-L cited them in his DAML proposal of 2000, but the SW gnored them.
>>
>> By the way, two commercial companies *based* on Prolog + RDBs are
>> Mathematica and Experian. Mathematica started with Prolog as their
>> underlying reasoning engine in the '80s, and they have developed the
>> foundation into a very rich logic-programming system that uses RDBs
>> for external storage.
>>
>> Experian uses Prolog + RDBs for Big Data -- much bigger than any
>> application that uses RDF + OWL. They compute everybody's credit
>> rating on a daily basis with every imaginable input they can find.
>> They use Prolog so heavily that they bought the Prologia company.
>>
>> MB
>>> > But I have to add that the transition between data model and ontology
>>> > is fluent. In practice, you often have to make compromises - for
>>> example to
>>> > enable better querying or because knowledge and application data
>>> cannot be
>>> > untied easily.
>>
>> I agree. And those issues were addressed in the 3-schema strategy of
>> of the original ANSI/SPARC TR in 1978. The conceptual schema -- which
>> is very close, if not identical, to what we now call formal ontology --
>> was at the heart of the proposal. The physical schema, which is very
>> close, if not identical to what is called the data model, specifies
>> the data formats, layout, and structure. The application schema
>> specifies the APIs of the software.
>>
>> I also agree that the detailed ontologies will often use primitives
>> and operations that have a simple mapping to the preferred data model.
>> That is another reason why I have recommended an underspecified upper
>> level ontology with families of "microtheories" for more specialized
>> ontologies that are optimized for different kinds of applications.
>>
>> But those issues get into details that we have discussed many
>> times before, and I won't repeat them now.
>>
>> John
>>
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>
>
>
> --
> Michel Dumontier
> Associate Professor of Bioinformatics, Carleton University
> Chair, W3C Semantic Web for Health Care and the Life Sciences Interest
> Group
> http://dumontierlab.com
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