David Genest

A platform allowing typed nested graphs : How CoGITo became CoGITaNT

This paper presents CoGITaNT, a software development platform for applications based on conceptual graphs. CoGITaNT is a new version of the CoGITo platform, adding simple graph rules and typed nested graphs with coreference links.

A content-search information retrieval process based on conceptual graphs

An intelligent information retrieval system is presented in this paper. In our approach, which complies with the logical view of information retrieval, queries, document contents and other knowledge are represented by expressions in a knowledge representation language based on the conceptual graphs introduced by Sowa. In order to take the intrinsic vagueness of information retrieval into account, i.e. to search documents imprecisely and incompletely represented in order to answer a vague query, different kinds of probabilistic logic are often used. The search process described in this paper uses graph transformations instead of probabilistic notions. This paper is focused on the content-based retrieval process, and the cognitive facet of information retrieval is not directly addressed. However, our approach, involving the use of a knowledge representation language for representing data and a search process based on a combinatorial implementation of van Rijsbergen’s logical uncertainty principle, also allows the representation of retrieval situations. Hence, we believe that it could be implemented at the core of an operational information retrieval system. Two applications, one dealing with academic libraries and the other concerning audiovisual documents, are briefly presented.

An Experiment in Document Retrieval Using Conceptual Graphs

In this paper an experiment using conceptual graphs to represent documents and queries is described. This paper is centred on the representation and answering language, a variant of simple CGs, and not on automatic indexing. Two important specificities of the experiment described in this paper are: first, it uses an existing general “ontology”, the thesaurus of RAMEAU, which is used in all the french universitary libraries; secondly, the CG indexation has been obtained from the existing indexation by adding a new “relational” level. The main goal of this paper is to show that it is possible to add, to general traditional document retrieval systems, a CG level, and that it is worthwhile to pay an additional cost, because, using CG for indexing documents and for representing queries, leads to significant improvements in the answers obtained.

CGs Applications: Where Are We 7 Years after the Fist ICCS?

The traditional distinction between theories (developed to tackle theoretical problems) and applications (based on theories and realized to help a user) is blurred in computer science. To experiment their theories, computer scientists often write programs. This paper focuses on the features that make such a program an application (also in its software engineering meaning). The discussion is more specifically aimed at artificial intelligence applications and especially conceptual graphs applications presented in ICCS papers, and the importance of applications for a scientific domain.

A Pure Graph-Based Solution to the SCG-1 Initiative

This paper answers the SCG-1 initiative. The room allocation problem provided has been solved in a generic and automatic way. The solution is based on a totally declarative formal model. Basic constructs are simple graphs and the fundamental operation for doing reasonings is the graph morphism known as projection. The other formal constructs are rules and constraints defined in terms of simple graphs. The modeling framework built upon the formal model allows one to describe a problem with asserted facts, rules representing implicit knowledge about the domain, validity constraints and rules transforming the world. A prototype implementing this framework has been built upon the tool CoGITaNT. It has been used to test our modelization of the room allocation problem.

ONTOLOGICAL COGNITIVE MAP

A cognitive map model provides a graphical representation of an influence network between concepts. One drawback of this model is that large cognitive maps are difficult to exploit and understand. This paper introduces an ontological cognitive map model that enables the designer to organize concepts in an ontology. On the one hand, this model provides an ontological influence mechanism that shows the influence from any concept of the ontology to any other according to the map. The map is then easier to exploit. On the other hand, the ontology is used for providing a synthetical view of a map. The map is then easier to understand.

Ontological Cognitive Map

A cognitive map model provides a graphical representation of an influence network between concepts. One drawback of this model is that large cognitive maps are difficult to exploit and understand.This paper introduces an ontological cognitive map model that enables the designer to organize concepts in an ontology. On one hand, this model provides an ontological influence mechanism that shows the influence from any concept of the ontology to any other according to the map. The map is then easier to exploit. On the other hand, the ontology is used as a scale for providing a synthetical view of a map. The map is then easier to understand.

Contextual Cognitive Map

The model of cognitive maps introduced by Tolman [1] provides a representation of an influence network between notions. A cognitive map can contain a lot of influences that makes difficult its exploitation. Moreover these influences are not always relevant for different uses of a map. This paper extends the cognitive map model by describing the validity context of each influence with a conceptual graph. A filtering mechanism of the influences according to a use context is provided so as to obtain a simpler and more adjusted map for a user. A prototype that implements this model of contextual cognitive map has been developed.

Modélisation, classification et propagation dans des réseaux d'influence

The cognitive map model provides a user a solution to visualize the influences between different notion, and to compute the propagation of influences on a target. Like cognitive maps, our model offers a graphical representation of influences between notions. The distinctive feature of our model is that on a unique support, each notion is precisely defined by conceptual graphs. The combination of operations of cognitive maps and operations of conceptual graphs provides a powerful method to make decision. Firstly, the definition of a notion and the projection provides a solution to compute semantically linked notions. Secondly, original propagations can be computed from such semantically linked notions.

Synthesis of Cognitive Maps and Applications

Cognitive maps are a knowledge representation model that describes influences between concepts. Their building is usually done by many people. This is a difficult task for them since they have to agree on every aspect of the map. This article proposes a new method to allow these people to be the designers of their own cognitive maps. A process, called synthesis, builds then a single cognitive map from this set of maps. The divergences in the maps due to the different points of view have to be solved. To do so, preferences on the designers are defined, they are used to favor the knowledge brought by some designer over the other ones.