<p>John Sowa (probably the foremost expert on knowledge representation) gives a thorough discussion of the subject here:
<a href="http://www.jfsowa.com/ontology/causal.htm" rel="nofollow">Sowa</a></p>

CC BY-SA 3.0
2015-08-27T20:47:21.753Z
2015-08-27T20:47:21.753Z
NietzscheanAI
<p>You are probably speaking about something like a <a href="https://en.wikipedia.org/wiki/Process_ontology" rel="nofollow">process ontology</a>. Lately, that research has focused (moved?) on <a href="https://scholar.google.com/scholar?hl=en&amp;q=semantic+workflows" rel="nofollow">semantic workflows</a>, e.g. to model processes in science, related with reproducibility. Similarly, we can also find the application of <a href="https://scholar.google.com/scholar?q=business+process+ontology" rel="nofollow">ontologies to business processes</a>.</p>

<p>For more classical (lower level?) approaches, you may be interested in the <a href="https://en.wikipedia.org/wiki/Homoiconicity" rel="nofollow">homoiconicity</a> property of programming languages like Lisp. In such a case these "networks" usually take the shape of parse trees, i.e. the representation is at a syntactic level and semantics are implicit, e.g. loops and recursive calls are still just a syntactic tree, even if the execution would not result into a tree.</p>

CC BY-SA 3.0
2015-01-29T11:08:01.820Z
2015-01-29T11:08:01.820Z
Trylks
<p>I know that knowledge about relationships between things can be represented using ontologies and stored in some sort of file or database system.</p>

<p>Can a network of procedural knowledge also be created in this way? Such that complex algorithms can be defined and stored efficiently, translated into other languages and forms (such as finite state machines or machine language), <a href="http://en.wikipedia.org/wiki/Belief_revision" rel="nofollow">changed</a>, and form the basis for other AI axioms?</p>

<p>i.e. <a href="http://en.wikipedia.org/wiki/Procedural_Reasoning_System" rel="nofollow">Procedural Reasoning Systems</a> -- how would a Knowledge Area (KA) be represented as a cognitive primitive in a computer system?</p>

<graph-theory><machine-learning><ai.artificial-intel><db.databases><knowledge-representation>
Representation of procedural knowledge
Josh
# Representation of procedural knowledge

(Asked by: username_0 on 2014-04-09)

I know that knowledge about relationships between things can be represented using ontologies and stored in some sort of file or database system.

Can a network of procedural knowledge also be created in this way? Such that complex algorithms can be defined and stored efficiently, translated into other languages and forms (such as finite state machines or machine language), [changed](http://en.wikipedia.org/wiki/Belief_revision), and form the basis for other AI axioms?

i.e. [Procedural Reasoning Systems](http://en.wikipedia.org/wiki/Procedural_Reasoning_System) -- how would a Knowledge Area (KA) be represented as a cognitive primitive in a computer system?

**Tags:** graph-theory, machine-learning, ai.artificial-intel, db.databases, knowledge-representation


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## 2 answers

### Answer by username_1 on 2015-08-27. Score: 1

John Sowa (probably the foremost expert on knowledge representation) gives a thorough discussion of the subject here:
[Sowa](http://www.jfsowa.com/ontology/causal.htm)


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### Answer by username_2 on 2015-01-29. Score: 1

You are probably speaking about something like a [process ontology](https://en.wikipedia.org/wiki/Process_ontology). Lately, that research has focused (moved?) on [semantic workflows](https://scholar.google.com/scholar?hl=en&q=semantic+workflows), e.g. to model processes in science, related with reproducibility. Similarly, we can also find the application of [ontologies to business processes](https://scholar.google.com/scholar?q=business+process+ontology).

For more classical (lower level?) approaches, you may be interested in the [homoiconicity](https://en.wikipedia.org/wiki/Homoiconicity) property of programming languages like Lisp. In such a case these "networks" usually take the shape of parse trees, i.e. the representation is at a syntactic level and semantics are implicit, e.g. loops and recursive calls are still just a syntactic tree, even if the execution would not result into a tree.


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