RE: What is a Knowledge Graph? CORRECTION

Hi Mike,


What we collect are facts, not knowledge. This fact collection is to be done during the entire existence of a process plant.

Once you have enough facts you might start extracting knowledge from them, for example which kind of pump seal is best in a given service, or what to do to optimize energy usage.

This derivation may take place by reasoning, statistical analysis, etc.


ISO 15926 deals with facts by excluding modalities by adopting  <> On the Plurality of Worlds of  <> David K. Lewis as summarized in . 

This allows for modeling designed objects in a separate possible world in the same manner as in the real world.


Regards, Hans 


From: Mike Bergman <> 
Sent: dinsdag 2 juli 2019 09:49
To:; 'Patrick J Hayes' <>; 'ProjectParadigm-ICT-Program' <>
Cc: 'Dave Raggett' <>; 'Paola Di Maio' <>; 'Amirouche Boubekki' <>; 'Chris Harding' <>; 'xyzscy' <>; 'semantic-web' <>
Subject: Re: What is a Knowledge Graph? CORRECTION


Hi All,

My take on the question:


On 6/25/2019 11:40 PM, <>  wrote:

Hi Pat,


+1 , that’s why we (the process industries) have an upper ontology, defined in ISO 15926-2 <> , with 218 entity types and the reference data library of ISO 15926-4 <>  with 39,000 classes. 

Application data are mapped to templates (212 small models, each using some of those 218 entity types), in RDF, validated with SHACL, and stored in a triple store.

Although this doesn’t cover the entire universe, it does cover the technical and activity life-cycle information of a process plant (oil, chemical, food, etc), integrated from cradle to grave.


Regards, Hans


From: Patrick J Hayes  <> <> 
Sent: dinsdag 25 juni 2019 19:22
To: ProjectParadigm-ICT-Program  <> <>
Cc: Dave Raggett  <> <>; Paola Di Maio  <> <>; Amirouche Boubekki  <> <>; Chris Harding  <> <>; xyzscy  <> <>; semantic-web  <> <>
Subject: Re: What is a Knowledge Graph? CORRECTION



On Jun 23, 2019, at 5:35 PM, ProjectParadigm-ICT-Program < <>> wrote:


Again, let us look at the issue at hand. Artificial intelligence requires we represent knowledge in some format. All forms brought to the fore so far stick to a pretty simple way of representing knowledge.


Most (all?) of the KR proposals put forward in AI or cognitive science work have been some subset of first-order predicate logic, using a variety of surface notations. There are some fairly deep results which suggest that any computably effective KR notation will not be /more/ expressive than FO logic. So FOL seems like a good ‘reference’ benchmark for KR expressivity.



What we should be looking for is a generalized form in which objects can be linked. The graph is an obvious form.

But we are focusing to much on the nuts and bolts level.


Since it is the generally accepted intention to use AI in all walks of professional, commercial, personal and academic life, we should be looking at the various ways of representing knowledge.


Otherwise we end up creating knowledge representation silos.


Avoiding KR silos was one of the primary goals of the entire semantic-web linked-data initiative. But this has many aspects. First, we need to agree to all use a common basic notation. Triples (=RDF =Knowledge Graph =JSON-LD) has emerged as the popular choice. Getting just this much agreement has taken 15 years and thousands of man-hours of strenuous effort and bitterly contested compromises, so let us not try to undo any of that, no matter what the imperfections are of the final choice. 


The next stage, which we are just getting started on, involves agreeing on a common vocabulary for referring to things, or perhaps a universal mechanism for clearly indicating that your name for something means the same as my name for that same thing. This seems to be much harder than the semantic KR pioneers anticipated.


The third stage involves having a global agreement on the ontological foundations of our descriptions, what used to be called the ‘upper level ontology’. This is where we get into actual metaphysical disagreements about the nature of reality (are physical objects extended in time? How do we handle vague boundaries? What are the relationships between written tokens, images, symbols, conventions and the things they represent? What is a ‘background’? What is a ‘shape’? Is a bronze statue the same kind of thing as a piece of bronze? What changes when someone signs a contract? Etc. etc., etc.) This is where AI-KR and more recently, applied ontology engineering (not to mention philosophy) has been working for the past 40 or 50 years, and I see very little hope of any clear agreements acceptable to a large percentage of the world’s users. 


Category theory diagrams, graphs and Feynman diagrams are three well known forms of representing knowledge graphs, but only in semantic web technologies we specify tuples, a restrictive form of representation.


Category diagrams and Feynman diagrams are meaningful only within highly restricted and formal fields (category theory and quantum physics, respectively) so have little to do with general KR. If your point is that diagrams are useful, one can of course point to many examples of them being useful to human users, but this does not make them obviously useful in computer applications. 


Tuples are not more restrictive than graphs, since a collection of tuples is simply one way to implement a graph. Tuple stores ARE graphs. 


Best wishes


Pat Hayes


Milton Ponson
GSM: +297 747 8280
PO Box 1154, Oranjestad
Aruba, Dutch Caribbean
Project Paradigm: Bringing the ICT tools for sustainable development to all stakeholders worldwide through collaborative research on applied mathematics, advanced modeling, software and standards development



On Sunday, June 23, 2019, 3:57:01 AM ADT, Paola Di Maio < <>> wrote: 





Chunks are also used in NLP (which is part of/related to CS either way)

aka tokens

Various useful references come up on searching chunks as tokens





On Sun, Jun 23, 2019 at 1:12 AM Dave Raggett < <>> wrote:


On 22 Jun 2019, at 14:54, Amirouche Boubekki < <>> wrote:


Le ven. 21 juin 2019 à 16:27, Dave Raggett < <>> a écrit :

Researchers in Cognitive Science have used graphs of chunks to represent declarative knowledge for decades, and chunk is their name for an n-tuple.


I tried to lookup "graph of chunks" related to cognitive science. I could not find anything interesting outside this white paper about "accelerating science" [0] that intersect with my goals.


[0]  <>


Chunks are used on cognitive architectures, such as ACT-R, SOAR and CHREST, and is inspired by studies of human memory recall, starting with George Miller in 1956, and taken further by a succession of researchers. Gobet et al. define a chunk as “a collection of elements having strong associations with one another, but weak associations with elements within other chunks.” Cognitive Science uses computational models as the basis for making quantitive descriptions of different aspects of cognition including memory and reasoning. There are similarities to Frames and Property Graphs.


Dave Raggett < <>>  <>

W3C Data Activity Lead & W3C champion for the Web of things 









Virusvrij.  <> 


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Received on Tuesday, 2 July 2019 10:32:10 UTC