The notion of role in conceptual modelling

In this article we analyse the notion of knowledge role. First of all, we present how the relationship between problem solving methods and domain models is tackled in different approaches. We concentrate on how they cope with this issue in the knowledge engineering process. Secondly, we introduce several properties which can be used to analyse, characterise and define the notion of role. We evaluate and compare the works exposed previously following these dimensions. This analysis suggests some developments to better exploit the relationship between reasoning and domain knowledge. We present them in a last section.

Un mod{è}le de base de connaissances terminologiques

In the present paper, we argue that Terminological Knowledge Bases (TKB) are all the more useful for addressing various needs as they do not fulfill formal criteria. Moreover, they intend to clarify the terminology of a given domain by illustrating term uses in various contexts. Thus we designed a TKB structure including 3 linked features: terms, concepts and texts, that present the peculiar use of each term in the domain. Note that concepts are represented into frames whose non-formal description is standardized. Associated with this structure, we defined modeling criteria at the conceptual level. Finaly, we discuss the situation of TKB with regard to ontologies, and the use of TKB for the development of AI systems.

Evaluating the Complementarity of Taxonomic Relation Extraction Methods Across Different Languages

Modern information systems are changing the idea of "data processing" to the idea of "concept processing", meaning that instead of processing words, such systems process semantic concepts which carry meaning and share contexts with other concepts. Ontology is commonly used as a structure that captures the knowledge about a certain area via providing concepts and relations between them. Traditionally, concept hierarchies have been built manually by knowledge engineers or domain experts. However, the manual construction of a concept hierarchy suffers from several limitations such as its coverage and the enormous costs of its extension and maintenance. Ontology learning, usually referred to the (semi-)automatic support in ontology development, is usually divided into steps, going from concepts identification, passing through hierarchy and non-hierarchy relations detection and, seldom, axiom extraction. It is reasonable to say that among these steps the current frontier is in the establishment of concept hierarchies, since this is the backbone of ontologies and, therefore, a good concept hierarchy is already a valuable resource for many ontology applications. The automatic construction of concept hierarchies from texts is a complex task and much work have been proposing approaches to better extract relations between concepts. These different proposals have never been contrasted against each other on the same set of data and across different languages. Such comparison is important to see whether they are complementary or incremental. Also, we can see whether they present different tendencies towards recall and precision. This paper evaluates these different methods on the basis of hierarchy metrics such as density and depth, and evaluation metrics such as Recall and Precision. Results shed light over the comprehensive set of methods according to the literature in the area.