Texts/Textes in areas (2024-03-15)

Annoter un ensemble de documents informels à l'aide de représentations formelles appelle plusieurs questions qui doivent trouver une réponse si l'on veut développer un système cohérent. Ces questions sont liées à la forme et à l'objet des représentations retenues, à la nécessité d'utiliser de la connaissance indépendante du contenu des documents (ontologies, connaissance de contexte) et au statut du système résultant (grande base de connaissance ou éléments de connaissance distribués). Ces questions sont décrites et illustrées par l'annotation de résumés d'articles en génétique moléculaire.

Improving information retrieval is annotation¹s central goal. However, without sufficient planning, annotation - especially when running a robot and attaching automatically extracted content - risks producing incoherent information. The author recommends answering eight questions before you annotate. He provides a practical application of this approach, and discusses applying the questions to other systems.

Modeling often concerns the translation of informal texts into representations. This translation process requires support for itself and for its traceability. We pretend that inserting a terminology between informal textual documents and their formalization can help to serve both of these goals. Modern terminology extraction tools support the formalization process by using terms as a first sketch of formalized concepts. Moreover, the terms can be employed for linking the concepts and the textual sources. They act as a powerful navigation structure. This is exemplified through the presentation of a fully implemented system.

Annoter un ensemble de documents informels à l'aide de représentations formelles appelle plusieurs questions qui doivent trouver une réponse si l'on veut développer un système cohérent. Ces questions sont liées à la forme et à l'objet des représentations retenues, à la nécessité d'utiliser de la connaissance indépendante du contenu des documents (ontologies, connaissance de contexte) et au statut du système résultant (grande base de connaissance ou éléments de connaissance distribués). Ces questions sont décrites et illustrées par la tentative d'annotation de résumés d'articles en génétique moléculaire.

This article deals with traceability in sotfware engineering. More precisely, we concentrate on the role of terminological knowledge the mapping between (informal) textual requirements and (formal) object models. We show that terminological knowledge facilitates production of traceability links, provided that language processing technologies allow to elaborate semi-automatically the required terminological resources. The presented system is one step towards incremental formalization from textual knowledge.

This article deals with traceability in knowledge repositories. More precisely, we concentrate on the role of terminological knowledge in the mapping between (informal) textual requirements and (formal) object models. We show that terminological knowledge facilitates the production of traceability links, provided that language processing technologies allow to elaborate semi-automatically the required terminological resources. The presented system is one step towards incremental formalization from textual knowledge. As such, it is a valuable tool for building knowledge repositories.

Knowledge engineering often concerns the translation of informal knowledge into a formal representation. This translation process requires support for itself and for its We pretend that inserting a terminological structure between informal textual documents and their formalization serves both of these goals. Modern terminology extraction tools support the process where the terms are a first sketch of formalized concepts. Moreover, the terms can be used for linking the concepts and the pieces of texts. This is exemplified through the presentation of an implemented system.

The Co₄ system is dedicated to the representation of formal knowledge in an object and task based manner. It is fully interleaved with hyper-documents and thus provides integration of formal and informal knowledge. Moreover, consensus about the content of the knowledge bases is enforced with the help of a protocol for integrating knowledge through several levels of consensual knowledge bases. Co₄ is presented here as addressing three claims about corporate memory: (1) it must be formalised to the greatest possible extent so that its semantics is clear and its manipulation can be automated; (2) it cannot be totally formalised and thus formal and informal knowledge must be organised such that they refer to each other; (3) in order to be useful, it must be accepted by the people involved (providers and users) and thus must be non contradictory and consensual.

A protocol and architecture are presented in order to achieve consensual knowledge bases (i.e. bases in which knowledge is expressed in a formal language and which are considered as containing the state of the art in some research area). It assumes that the construction of the base must and can be achieved collectively. The architecture is based on individual workstations which provide support for developing a knowledge base: formal expression of knowledge through objects, tasks and qualitative equations annotated with hypertext nodes and links. It also provides tools for detecting similarities and inconsistencies between pieces of knowledge. These bases can be grouped together in order to constitute a new reference knowledge base. The process for constructing this last base mimics the submission of articles to peer-reviewed journals. This is achieved through a protocol for submitting knowledge to the group base, confronting it with the content of that base, amending it accordingly, reviewing it by the other knowledge bases and finally incorporating it. The system is to be used by researchers in the field of genome sequencing.