Or: baby oil vs. palm oil. Hmm. (01)
>-----Original Message-----
>From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-
>bounces@xxxxxxxxxxxxxxxx] On Behalf Of Barkmeyer, Edward J
>Sent: Tuesday, April 02, 2013 2:11 PM
>To: [ontolog-forum]
>Subject: Re: [ontolog-forum] Ontology for Climate Change - need input
>
>There is nothing inaccurate about having the term "pineapple tree" in your
>ontology. The inaccuracy would be in saying that it is a subtype of "tree",
>assuming that we all agree on the definition of "tree". The assumption that
>the
>word "tree" appearing in the term "pineapple tree" implies some well-defined
>relationship is unwarranted. This is the kind of thing that comes from trying
>to
>guess what is meant by looking at natural language cues. Sometimes you guess
>wrong!
>
>Fabian Neuhaus's favorite example is "fake diamond". It is by definition NOT a
>kind of "diamond".
>
>-Ed
>
>> -----Original Message-----
>> From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-
>> bounces@xxxxxxxxxxxxxxxx] On Behalf Of Jack Park
>> Sent: Tuesday, April 02, 2013 1:32 PM
>> To: [ontolog-forum]
>> Subject: Re: [ontolog-forum] Ontology for Climate Change - need input
>>
>> If it is known to ontologists that common vocabulary includes the term
>> "pineapple tree", then is there not a way to engineer ontologies to account
>> for such "inaccurate" statements and still be useful?
>>
>> Jack
>>
>> On Tue, Apr 2, 2013 at 8:45 AM, doug foxvog <doug@xxxxxxxxxx> wrote:
>> > 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.
>> >
>> > 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.
>> >
>> > Predicates for temperature ranges for growth, flowering, and fruiting
>> > of plant species should be in an ontology dealing with climate change.
>> >
>> > Do you have a link to your current ontology?
>> >
>> > -- doug foxvog
>> >
>> > 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.
>> >
>> >> To illustrate the point, consider this evidence:
>> >> http://technoracle.blogspot.ca/2013/04/finally-concrete-proof-of-glob
>> >> al-warm
>> >> ing.html
>> >
>> >
>> >
>> >> 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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