
## 3. The Sg Family: Extensions Of Simple Conceptual Graphs

This section is devoted to an overview of the different models composing the SG family. We will first outline the main motivations for our graph-based approach of knowledge represent ation.

## 3.1  Knowledge Representation And Reasonings With Graphs

A modeling viewpoint  From a modeling viewpoint, we see two essential properties in the simple graph model. The objects, simple graphs, are easily understandable by an enduser (a knowledge engineer or even an expert). And reasonings are easily understandable too, for two reasons: projection is a graph matching operation, thus easily interpretable and visualisable; and the same language is used at interface and operational levels.

Although there is a gap between the theoretical foundations studied here and a language usable in real applications, we would like to briefly mention two projects in which these properties have been exhibited. The first one is an experiment in document retrieval done by Genest (2000). In this work, conceptual graphs are used to define a language for indexing and querying documents. Concept types are taken from the thesaurus of RAMEAU (about 400 000 types), a documentary language used in most french public and universitary libraries.

The experiment proved the feasibility of the proposed system (w.r.t. computing time) and an improved relevance w.r.t.  to the existing system based upon RAMEAU, mainly due to the use of semantic relations instead of keywords only.  One side effect was also to prove the interest of simple graphs from a modeling viewpoint. Indeed, their graphical properties enabled to build an indexing/querying tool that was considered as easy to use for the indexers. The users were master humanities students, not aware from conceptual graphs neither from RAMEAU; with the software and an indexing guide, they became quickly able to build indexations, that were considered of high quality by a senior librarian.

The second project takes place in knowledge engineering (Bos, Botella, & Vanheeghe, 1997). Its purpose is the construction of tools for modeling and simulating human organizations, as emergency procedures for instance. One main difficulty in knowledge engineering is to validate a modeling, i.e. to check that the expert reasoning is correctly modeled. This validation is usually done when the design is achieved, here by simulating the constructed modeling of the organization. At this final stage, modifications are very costly. The key idea of the project is to overcome this difficulty by giving the expert the ability to use simulation inside the design cycle as a mean of enriing and building his modeling. This implies that the chosen modeling language enables the expert to follow reasonings step by step, directly on his own modelization. It was decided to build such a language upon conceptual graphs General conceptual graphs equivalent to FOL were not considered as good candidates because they are indeed a diagrammatic system of logic that is not at the expert level. Instead, the language was grounded upon simple graphs and extensions (such as nestings of graphs)
keeping their readability. Operations mixed simple graph deduction (i.e. projection) with non declarative procedures. First experiments were conclusive.

A computational viewpoint  From a computational viewpoint, we think that graphbased reasonings, benefitting from graph-theoretical results, can bring an interesting perspective to logic programming. By example, the equivalence between SG projection and