On 30 Jan 2014, at 23:51, John F Sowa wrote: (01)
> Dear Christoph, Ron, and Jack,
>
> CL
>> In short: facing the reality of Big Data and the need for increasingly
>> intelligent services, (how) can ontology-based tools and techniques help?
>
> That question gets to the heart of the matter.
>
> But before we can answer the question "How?", we need to ask what
> we mean by "ontology-based tools" and whether the current tools
> that use the tag 'ontology' are the most relevant to ontology.
>
> RW
>> The IBM team provided Watson with millions of documents, including
>> dictionaries, encyclopedias, and other reference material that it
>> could use to build its knowledge. Although Watson was not connected
>> to the Internet during the game, it contained 200 million pages of
>> structured and unstructured content consuming four terabytes of
>> disk storage, including the full text of Wikipedia.
>
> That is using "Big Data" to tackle the problem of "Big Data".
> The following diagram from Wikipedia summarizes the steps in
> Watson's approach to answering a question:
>
>
>http://upload.wikimedia.org/wikipedia/commons/thumb/4/41/DeepQA.svg/800px-DeepQA.svg.png
>
> None of the boxes in that diagram explicitly mention ontology. Among
> the many resources, they do use DBpedia, which does use RDF and OWL.
>
> But the classifications used for DBpedia don't use features of OWL
> that go beyond Aristotle's syllogisms. An automated or at least semi-
> automated system that generates hierarchies without all the hand coding
> of OWL could be more useful -- for example, Formal Concept Analysis:
>
> http://www.upriss.org.uk/fca/fca.html
>
> Note the applications of FCA to construct lattices automatically from
> resources such as WordNet and Roget's Thesaurus. FCA has also been
> used to check the consistency of OWL ontologies. But if you can derive
> the hierarchy automatically, why bother with a method that depends on
> hand-coding, such as OWL?
>
> JR
>> Once you have enabled a semantic model of me then a machine can find
>> any and all instances in Big Data that are relevant to me...
>
> Yes. But it's important to have automated or semi-automated ways of
> creating such models or deriving them from the data. I've mentioned
> the kinds of things we've been doing at VivoMind. For a reminder,
> see http://www.jfsowa.com/talks/goal7.pdf
>
> But to show other systems that also do similarity matches by automated
> methods, see the excerpt below. Note that they use a triple store,
> but they create an associative memory that retrieves information by
> similarity. That's very different from the typical SW tools.
>
> Some people say "Tools are boring." I agree that YASWT (Yet Another
> Semantic Web Tool) is indeed boring. But the new tools implement
> ways of thinking about ontology that are totally different from the
> SW dogma. That is not boring. That can be revolutionary. (02)
Most big data app logic/algorithms are far more complex than can be written in
logic languages. That said, RDF has some nice features wrt wrt the
"variability" aspect of big data apps. You can think of it as supporting
schema-less data where you discover the ontology and can layer it over the data
after analysis of the data itself. According to Gartner many organizations
find the “variety” dimension of big data a much bigger challenge than volume or
velocity. When asked about the dimensions of data organizations struggle with
most, 49% answered variety, while 35% said volume, and 16% replied velocity.
Our CEO wrote an article for InformationWeek about this: (03)
http://www.informationweek.com/big-data/varietys-the-spice-of-life-andndash-and-bane-of-big-data/d/d-id/1112960 (04)
John talks about the "SW dogma", by which I assume he means the OWL DL dogma,
but I hope everyone realizes that there are many very practical use cases where
RDF/OWL solves a problem and where the DL dogma is not relevant - in our view
Big Data/Semantics is one of those use cases. The majority of our customers are
actually not members of the "SW dogma" club. That said, we do a lot of
inference over data ... it's just rules driven using SPIN rather than a
reasoner. So, in our experience an OWL DL reasoner is not the primary SW tool -
SPARQL is the primary SW tool (and by a long way). That's why SPIN is built
over SPARQL. (05)
I expect the academic world is where John and others who complain about "SW
dogma" spend their time. That dogma does exist, but it is not even close to
being the majority of enterprise uses of SW tech. (06)
Cheers,
David (07)
>
> Given that people have been spending 14 years to "educate" software
> developers about ontology, maybe we need some new thinking.
>
> John
> _____________________________________________________________________
>
> https://saffrontech.atlassian.net/wiki/display/saffron/The-SMB-Store
>
> The SMB (SaffronMemoryBase) Store is a different kind of data store.
> Sometimes referred to as an information or knowledge store rather than a
> data store because it stores weighted "associations" or "links" between
> things, the fundamental representation of knowledge. Similar to how a
> search (inverted) index stores a link between a keyword and a document,
> SMB also stores links between terms. However, SMB goes beyond storing
> links between keywords and documents. Like RDF (triple) stores, SMB can
> also store associations between 3 things. But again, SMB goes beyond
> just storing links between 3 things. SMB also stores association
> counts, i.e. the number of times a particular association has occurred.
> Associations are typically, but not limited to, stored as doubles
> (A-C) and/or triples (A-B-C), where triples provide additional context
> (B) to the link (A-C). Looking up association counts, at query time,
> allow you to very efficiently utilize frequency-based statistics to
> compute results. When compared with alternative database methods of
> computing association counts at query time, you will find that using a
> SaffronMemoryBase makes the impractical, practical and opens a new world
> of opportunities for innovative problem solving.
>
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