Please, if you read french, you could consult this link.

https://hal.inria.fr/hal-00906231/document.

I hope that the content of this paper helps you.r 

Hi Saba, 

Still in french, but could  the following document and reference  help you? 

Paquette, G. (2002). Modélisation des connaissances et des compétences. Montréal, Presses de l'université du Québec.

Cordialement, 

Pierre

A recent contribution with the state of the art in knowledge representation and its relation to semantic web is Rinke Hoekstra's PhD thesis (Chapter 2-3): Ontology Representation - Design Patterns and Ontologies that Make Sense (2009) http://dare.uva.nl/document/2/68623.

A less recent comparison between OOP Systems, Frame Systems and Description Logics (i.e. the logics on which the semantic web is constructed) can be found on: O. Lassila, D. McGuinness, The Role of Frame-Based Representation on the Semantic Web, Technical Report KSL-01-02, Knowledge Systems Laboratory, Stanford University, 2001, available on http://www.ksl.stanford.edu/people/dlm/etai/lassila-mcguinness-fbr-sw.html

Remaining in the domain of the semantic web, the de facto standard in representing rules is the spin project: http://spinrdf.org/.

For what it concerns "procedures", as many interpretations are possible, I limit myself to the (business) process perspective. UML, and BPMN notations are the most used in that field, but, maintaining the focus on ontologies, you could start by reading: Martin Hepp, Dumitru Roman: An Ontology Framework for Semantic Business Process Management, Proceedings of Wirtschaftsinformatik 2007  http://www.heppnetz.de/files/hepp-roman-an-ontology-framework-for-SBPM-WI2007.pdf

I think this link is helpful as a start:

http://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning

Hi Saba et al.,

I am very sure that you are aware of it, but hopefully I can point you in a few directions that you might not be familiar with (at the same time as promoting the technology to others who have not heard of it).

Certainly the largest following for knowledge representation "languages" is the Resource Description Framework (RDF), which is the model that powers both the Semantic Web and the Linked Data web. It's a W3C standard, which is a very well respected consortium. It has plenty of ontologies ready for use in different domains. It is also enriched by a large web of open data in the form of the linked open data (LOD) cloud. SPARQL is the most well used query language, that acts a little bit like SQL does to a relational database. The power of RDF really comes when you pair it with cross-domain hyperlinking, and that's where the Linked Open Data cloud comes in.

I'd very highly recommend everyone looking into it. Here are some links....

* Linked Open Data cloud: http://lod-cloud.net/

* The LOD2 project which promoted LOD to public and private organisations in Europe, and provided them with consultancy, tutorials and a bespoke stack of technologies: http://lod2.eu/

* SKOS (Simple Knowledge Organisation System), an RDF ontology for making frame-like data: http://www.w3.org/2004/02/skos/

* Rule Interchange Format (RIF): http://www.w3.org/2005/rules/wiki/Primer

* TRiG is very important for serialisation of Named Entities and RDF datasets: http://wifo5-03.informatik.uni-mannheim.de/bizer/trig/

* I've used TRiG quite a while ago with XLWrap which transforms Excel spreadsheets into RDF: http://xlwrap.sourceforge.net/

I hope that all helps, and is interesting!

Daniel

Following Daniel, who points in the direction of graphical knowledge representation, I would  suggest to have a look on Probabilistic Graphical Models (PGM) in general and Bayesian Networks (BN) as a special kind of PGMs. With BN, you are able to quantify the strength (or even the uncertainty) of relations between the nodes in your graphical model and perform several reasoning tasks to query your model as soon as you receive new input data. Furthermore, BNs represent (the relationships between different) knowledge (entities - the model nodes) very intuitively and allow you to combine informal (human) expert knowledge and data from different sources in a single model.

Here you find two cases of expert knowledge formalization by means of Bayesian Networks

https://www.researchgate.net/publication/265989016_About_Probabilistic_Graphical_Models_in_Probabilistic_Avalanche_Science_The_Case_of_Stop_or_Go

and

https://www.researchgate.net/publication/263651860_On_the_Formalization_of_Expert_Knowledge_A_Disaster_Management_Case_Study

In the references sections of these papers, you will find a number of further pointers to PGMs an BNs.

Mario

Conference Paper About Probabilistic Graphical Models in Probabilistic Avalan...

Conference Paper On the Formalization of Expert Knowledge: A Disaster Managem...

Adding to the comment by Mario, who kindly added to my comment... :-)

There has also been some interesting work on what is called "Probabilistic Logic Networks (PLN)", I'm not sure if this relates to Marios work, but I suspect it overlaps a little. PLNs were developed by Ben Goertzel, and has its basis in Bayesian Networks (with some work on Fuzzy Sets too). Ben Goertzel is one of the pioneers of the area of "Artificial General Intelligence" (i.e. general problem solving using Artificial Intelligence), which has got quite a lot of publicity lately with all this computational superintelligence risks media attention.

Here is an academic reference on PLNs:

Ben Goertzel, Matthew Iklé, Izabela Lyon Freire Goertzel, Ari Heljakka (2008). Probabilistic Logic Networks: A Comprehensive Conceptual, Mathematical and Computational Framework for Uncertain Inference. Springer. p. 333. ISBN 0-387-76871-8.

Also check out Ben Gortzels work on OpenCog:

http://opencog.org/

and some related work by the famous Stuart Russell on a language for handling probabilistic models:

Milch, Brian, et al. "1 BLOG: Probabilistic Models with Unknown Objects." Statistical relational learning (2007): 373.

Mario: Does this relate to your work in any way?

I think that these are very exciting times to be involved in artificial intelligence and knowledge engineering.

Daniel

Add-on: concerning your request for "some ... "Rule" "Frame" and "Procedure". Is there any more?", I can suggest the Book "Bayesian Artificial Intelligence" by Korb and Nicholson, who nicely introduce Bayesian Networks including a knowledge engineering process for building BN and much more ... here is the link: http://www.csse.monash.edu.au/bai/

Mario

To add to your list, after toying with Lisp, and Prolog, my team and I used OPS5 to develop AI/ES application for diagnosing  failure of computer boards