Re: Is scalability the key property of knowlege graph?

Thanks, Enrico! It's an interesting report, and certainly reinforces the 
scalability aspect: concluding that knowledge graphs are a manifestation 
of a new phenomenon - knowledge and data at scale.

Franconi Enrico wrote:
> You may find this summary about the current practice and research on 
> KGs interesting:
> https://www.juansequeda.com/blog/2018/09/18/trip-report-on-knowledge-graph-dagstuhl-seminar/
> And, yes, I believe that a KG is basically an RDFs graph grounded to 
> some sort of reality.
> In terms of KR practice, KGs are an amazing leap backwards to the good 
> old semantic nets of the 70ies.
> This is not to say that they don't play a useful role within Google 
> technologies or similar stuff.
> cheers
> --e.
>
>> Il giorno 13 giu 2019, alle ore 12:10, Chris Harding 
>> <chris@lacibus.net <mailto:chris@lacibus.net>> ha scritto:
>>
>> What is a knowledge graph?
>>
>> I looked it up in Wikipedia, and the definition seemed to be "What 
>> Google does". Reading a bit more widely, I came to the conclusion 
>> that it is a triple store to which someone attaches meaning. (Of 
>> course, this is most, if not all, triple stores.) What is interesting 
>> is the impressive amount of theory and practice, associated with the 
>> "knowledge graph" label, for using AI and other techniques to obtain 
>> transformations or measurements of the triple stores that add to the 
>> meaning that people attach to them.
>>
>> I found these articles helpful:
>> http://ceur-ws.org/Vol-2322/dsi4-6.pdf
>> https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526
>> https://content.iospress.com/articles/data-science/ds007
>>
>> xyzscy wrote:
>>> Thank you for your response. I think the KG term is spread by 
>>> GOOGLE, while I don’t how google implement it.  I used to think the 
>>> semantic network  is the key technology of KG,but google has never 
>>> statement that.
>>>> 在 2019年6月13日，下午2:46，Paola Di Maio <paola.dimaio@gmail.com 
>>>> <mailto:paola.dimaio@gmail.com>> 写道：
>>>>
>>>> Thank you for asking this,
>>>>
>>>> I  ll leave the experts to reply to scalability and other questions
>>>>
>>>> In general, much depends on the language one uses, which in turn
>>>> depends on the domain (which planet you come from)
>>>>
>>>> When I first studied knowledge engineering, the expression 
>>>> knowledge graph
>>>> was not in use at all. I was doing an MSc and studied the body of 
>>>> knowledge
>>>> from ESPRIT project (some folks on this list worked on it)
>>>> https://pdfs.semanticscholar.org/193e/b66909b0c87d5dbcdbd6b20d78ed93fc95a7.pdf 
>>>>
>>>>
>>>>  I d be curious to learn when such term knowledge graph came in use 
>>>> and who coined it
>>>>
>>>> I then heard it in relation to the SW and this list, and always 
>>>> tried to figure out what exactly
>>>> a KG is (in relation the wider Knowledge Representation domain I 
>>>> was studying)
>>>>
>>>> Knowledge graphs are a type of knowledge representation, and they 
>>>> can be visualized
>>>> graphically, or represented using algebra (again, depends on what 
>>>> planet you are on)
>>>> Engineers tend to use diagrams, others tend to use algebra
>>>>
>>>> But more importantly, is that they enable machine readability 
>>>> querying and computational manipulation of complex (combined) data 
>>>> sets, assuming knowledge is some kind of data in context, as some say.
>>>> I dont use the term knowledge graph much either.  Let's see if the 
>>>> KG folks can offer more info
>>>>
>>>> PDM
>>>> Knowledge Graph Representation
>>>> *Knowledge graphs* provide a unified format for representing 
>>>> *knowledge* about relationships between entities. A *knowledge 
>>>> graph* is a collection of triples, with each triple (h,t,r) 
>>>> denoting the fact that relation r exists between head entity h and 
>>>> tail en- tity t. http://ceur-ws.org/Vol-2322/dsi4-6.pdf
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> On Thu, Jun 13, 2019 at 1:40 PM 我 <1047571207@qq.com 
>>>> <mailto:1047571207@qq.com>> wrote:
>>>>
>>>>     Dear all:
>>>>
>>>>     When I first touch knowledge graph, I'm very confused.
>>>>     Different from the other AI theory,  it is not an pattern
>>>>     recognization algorithm which will  give some "output" given
>>>>     some "input"(such as classify algorithms) ,but a program
>>>>     language(such as owl,rdf) and database(such as neo4j) instead.
>>>>     So in my opinion, knowledge graph is more like a problem of
>>>>     engineering than mathematic theory.
>>>>
>>>>     Then I realized that different from the pattern recognization
>>>>     algorithm, the knowledge graph is created aimed at making the
>>>>     computes all over the world to communicate with each other with
>>>>     a common language, and I have a question: Is scalability the
>>>>     key property of knowledge graph?
>>>>
>>>>     There are many knowledge vaults edited by different
>>>>     language(such as owl,rdf ),but is it always hard to merge them
>>>>     and there is not a standard knowledge vault  on which  we can
>>>>     do advanced  development. So is it necessary to open a
>>>>     scalable  and standard knowledge vault so that everyone can
>>>>     keep extended it and make it more perfect just like linux
>>>>     kernel or  wiki pedia? What kind of knowledge should be
>>>>     contained in the standard knowledge vault so that it can be
>>>>     universal?  I imagine that the standard knowledge vault is an
>>>>     originator, and all of the other application copy the
>>>>     originator, then all of the other application can
>>>>     communicate under the same common sense, for example when a
>>>>     application decelerate ''night", all of the other
>>>>     application will know it's dark.
>>>>
>>>>     As I know, the knowlege graph is implement as a query service,
>>>>     but is it possible to implement it  as a program language,just
>>>>     like c++,java? In this way ,the compute can directly know
>>>>     nature language, and human can communicate with compute with
>>>>     nature language, also a compute can communicate with another
>>>>     compute with nature language.
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>
>> -- 
>> Regards
>>
>> Chris
>> ++++
>>
>> Chief Executive, Lacibus <https://lacibus.net/> Ltd
>> chris@lacibus.net
>>
>

-- 
Regards

Chris
++++

Chief Executive, Lacibus <https://lacibus.net> Ltd
chris@lacibus.net