Sylvain Delisle

INTERACTIVE SEMANTIC ANALYSIS OF TECHNICAL TEXTS

Sentence syntax is the basis for organizing semantic relations in TANKA, a project that aims to acquire knowledge from technical text. Other hallmarks include an absence of precoded domain‐specific knowledge; significant use of public‐domain generic linguistic information sources; involvement of the user as a judge and source of expertise; and learning from the meaning representations produced during processing. These elements shape the realization of the TANKA project: implementing a trainable text processing system to propose correct semantic interpretations to the user. A three‐level model of sentence semantics, including a comprehensive Case system, provides the framework for TANKAs representations. Text is first processed by the DIPETT parser, which can handle a wide variety of unedited sentences. The semantic analysis module HAIKU then semi‐automatically extracts semantic patterns from the parse trees and composes them into domain knowledge representations. HAIKUs dictionaries and main algorithm are described with the aid of examples and traces of user interaction. Encouraging experimental results are described and evaluated.

Invited Paper: Intelligent Data Mining Assistance via CBR and Ontologies

Most commercial data mining products provide a large number of models and tools for performing various data mining tasks, but few provide intelligent assistance for addressing many important decisions that must be considered during the mining process. In this paper, we propose the realization of a hybrid data mining assistant, based on the CBR paradigm and the use of an ontology, in order to empower the user during the various phases of the data mining process

Reengineering the knowledge component of a data warehouse-based expert diagnosis system

We describe the weaknesses of an existing expert diagnosis-recom mendation system we have developed for SMEs. In good part, these weaknesses are related to the fact that the system was not implemented with appropriate artificial intelligence techniques. We recently decided to tackle the problem and re-engineered the core of the system with the help of an up-to-date expert system shell. In the process, we revised the formaliza tion and reorganization of the systems expertise and developed a brand new knowledge base. We here describe the new system and the improvements made, and we identify ongoing and future developments.