Indexing Corporate Memories through Ontologies Djida Bahloul, Youssef Amghar, Pierre Maret INSA de Lyon – LISI, 7 avenue Jean Capelle. 69621, Villeurbanne – France. dbahloul@lisi.insa-lyon.fr, {amghar,maret}@insa-lyon.fr Abstract In the context of Knowledge Management, we carry out a Corporate Memories (CM) project for the Company CIRTIL 1 . Our purpose is to focus on the modelling of the application domain. It is built as a domain ontology with a structure supporting a semantic model based on ontological relationships. In this paper we, present our S 3 model which permits to model knowledge and to index documents. We also show how semantic indexing of technical documents can be improved by mean of the domain ontology. We show finally the interest of our model with the implementation of a prototype. Keywords Semantic indexing, ontology, corporate memory, knowledge modelling. 1. INTRODUCTION Corporate Memories (CM) require abilities to manage disparate information and heterogeneous sources in order to make knowledge accessible to the adequate users of the enterprise. CM must also consider the integration and the storage of knowledge contained into electronic documents or contained into knowledge bases. According to us, to permit the success of using and maintaining the CM, it is important to consider employees as the hard core of the system. Indeed, employees can participate to capture knowledge and to structure it into the CM. The objective of our research works aims to offer to users a methodological assistance and tools enabling the knowledge management. To achieve this objective, our approach takes advantage of both: the contribution of ontologies as proposed by the Artificial Intelligence (AI) community and the documentary indexing such as defined in the domain of the library science. The role of ontology is the representation and the modelling of knowledge. “An ontology is an explicit specification of a conceptualisation” [Gruber, 1993]. The role of documentary indexing is the matching between the represented knowledge and users’ queries. In this paper, we propose an indexing model which is more efficient than a simple taxonomy of concepts. Therefore, we build a domain ontology which has a significant capacity of expression thanks to the possibility of introducing semantic links, structural links and subsumption links between concepts. We show in this paper how to facilitate the indexing of technical documents through the ontology. We outline our approach for the construction of an ontology based on 1 CIRTIL : Centre d’Informatique Régional du Traitement de l’Information Lyonnais which supports this work. ontological relationships. This is followed in section 2 by a description of our model called S 3 allowing both representation and indexing of knowledge. In the section 3, we apply this model to build an ontology and to index the CM of a company. 2. RELATED WORKS AND POSITIONING Ontologies can constitute a component of a CM: they can be explored by the end-user to discover the organization processes and business objects of the enterprise (e.g. enterprise ontology), or to study methodically a specific technical domain (e.g. domain ontology), etc. Ontologies are then used as a coherent support to describe and to share knowledge. “Ontologies constitute the glue that binds knowledge subprocesses together. Ontologies open the way to move from a document-oriented view of Knowledge Management to a content-oriented view, where knowledge items are interlinked, combined, and used.» [Staab and al., 2001]. In fact, ontologies are used more and more in KBS (Knowledge Management System) development. For example, projects such as SHOE [Heflin and Hendler, 2000] and Ontobroker [Benjamins and Fensel, 1998] use ontologies to improve the searching abilities on the World Wide Web. Both systems provide logical reasoning based on ontological definitions. In [Gandon, 2001]; [Gandon and al, 2002], CoMMA project offers a solution to implement a CM based on ontologies and agent technology. It promotes a wide vision of the document retrieval issue that could be applied to several cases. The memory is composed of heterogeneous evolving documents, structured using semantic annotations expressed with concepts and relationships provided by a shared ontology. In others approaches, ontologies are exploited to organize the knowledge and to support computational design. For instance, the approach for ontology-based knowledge management [Staab and al., 2001] includes a tool suite and methodology for developing ontology-based Knowledge Management systems. Among these tools, OntoAnnotate tool allows users to create objects and describe them with their attributes and relationships. This outline of the state of art shows that modelling based-ontology is interesting in the frame of CM. Nevertheless, the difference between our approach and related works consists in using ontologies to carry out the documentary indexing of formalized knowledge. We understand by documentary indexing, "the operation which consists to describe and to characterize contents of documents by using representative concepts." In our approach, this operation is carried out by the ontology (instead of a documentary language). We argue that ontologies can guarantee a sufficient indexing because, they introduce a host of structural and conceptual