This paper is organized as follows. First, we start with a brief recall of basic notions and related literature. Second, we describe the structuration of knowledge in Menelas, and the way it rests on a CTL. Third, we show how UMLS has been used to initiate CTL building, then illustrate how the CTL structures the knowledge acquisition process. Finally, we discuss the advantages and further issues associated with this approach.

The elicitation of the ontology -- i.e. the objects of a domain -- is a key issue of conceptual modelling and therefore of knowledge acquisition. The Conceptual Graph Theory provides a knowledge representation formalism to be used in knowledgebased systems with an explicit "type lattice" to account for the ontology. Since knowledge is in most AI applications non formal, it has to be normalized to ensure that the formal exploitation of its representation conforms to its meaning in the domain. Noting the intensional nature of types, which reflect the essences of the objects they denote, this normalization relies on a commitment on type definitions by necessary and sufficient conditions at the knowledge level. Our claim is that the taxonomic structure that accounts for the intensional nature of the ontology can be nothing but a tree, precluding tangled taxonomies. From this starting point, we derive methodological principles to constrain the acquisition of the "type tree", thus...

The overall goal of Menelas is to provide better access to the information contained in natural language patient discharge summaries, through the design and implementation of a pilot system able to access medical reports through natural languages. A first, experimental version of the Menelas indexing prototype for French has been assembled. Its function is to encode free text PDSs into both an internal representation and ICD-9-CM nomenclature codes. A preliminary evaluation shows the potential for reasonable coverage and precision. The Menelas prototype will be enhanced and extended into a pilot system which will be tested in two hospital sites. Keywords: Natural Language Processing; Patient Record; Knowledge-Based Systems; Information Retrieval; ICD-9-CM; Conceptual Graphs.

this paper, we examine some methodological and theoretical principles to enforce this conformity. These principles result from our experience in Menelas, a medical language understanding project.

Controlled medical terminologies (CMTs) are playing central roles in clinical information systems and medical knowledge resource applications. As these terminologies grow, they are able to support more complex tasks but require more intensive efforts to create and maintain them. Several terminologies are evolving into knowledge bases of medical concepts. The knowledge they include is being used to support distributed cognition in two forms: complex medical decisions involving multiple people and applications, and coordination of maintenance

We have examined a number of methods for structuring and processing free text documents including formal concept analysis, knowledge spaces, and conceptual graphs. Lattice structures over documents has been previously shown to be efficient and effective for information retrieval. We propose to integrate concept analysis, knowledge spaces and self-organisation in general and apply these methods to patient discharge summaries. We intend to demonstrate that a combination of conceptual graph representation of medical texts and document clustering techniques can provide efficient information retrieval and at the same time improve the quality of information returned to users. 1 Introduction When paper medical records are examined in detail, the types of data they contain can be broadly classified into two main categories: structured data and unstructured data. Unstructured data in the form of natural language forms a larger portion of the medical record [SBS + 82] because it is the medium...

This paper surveys some of the medical applications using Conceptual Graphs, briefly examines Conceptual Graphs and proposes the Peirce Conceptual Graphs workbench as a general tool for supporting medical information modeling. 1 Introduction