In the context of the Palette project aimed at enhancingallindividual and organizational learning in Communities of Practice (CoPs), we are developing Knowledge Management (KM) services. Our approach is based on an ontology dedicated to CoPs and built from analysis of information sources about eleven CoPs available in Palette project. This ontology aims both at modeling the members of the CoP and at annotating the CoP knowledge resources. The paper describes our method for building this ontology, its structure and contents and it analyses our experience feedback from the cooperative building of this ontology.

Semantic web approach seems interesting for supporting content mining of millions of patents accessible through the Web. In this paper, we describe our approach for generating semantic annotations on patents, by relying on the structure and on a semantic representation of patent documents. We use both the structure of the patent documents and their textual contents processed by Natural Language Processing (NLP) tools. This method, primarily aimed at helping biologists use patent information can be generalized to all kinds of domains or of structured documents.

This paper presents ASCO3 algorithm, aimed at finding mappings between entities of two ontologies represented in OWL DL/Lite. ASCO3 algorithm searches the maximal common subgraph of two graphs representing two ontologies, and relies on an algorithm of search of the maximal clique of their association graph.

Web usage mining can play an important role in supporting the navigation on the future Web. In fact detection of common or professional profiles allows browsers and web sites to personalise the user session and to recommend specific resources to the interested people. Semantic web approach seems interesting for this task. We propose in this paper a generic approach for profile detection relying on semantic web technologies. It takes advantages from ontologies, semantic annotations on web resources and inference engines.

An approach for managing knowledge in an organization in the new infrastructure of Semantic Web consists of building a corporate semantic web (CSW). The main components of a CSW are (i) evolving resources distributed over an intranet and indexed using (ii) semantic annotations expressed with the vocabulary provided by (iii) a shared ontology. However, changes in the operating environment may lead to some inconsistencies in the system and they result in need of modifications of the CSW components. These changes need to be evolved and well managed. In this paper we present a rule‐based approach allowing us to detect and correct semantic annotation inconsistencies. This rule‐based approach is implemented in the CoSWEM system enabling to manage the evolution of such a CSW, especially to address the evolution of semantic annotations when its underlying ontologies change.

The information explosion especially on the World Wide Web has made it become the most immense information source and a gold mine for corporations. In the field of technology monitoring, it is very important to be able to get relevant information from heterogeneous sources on the Web. The arrival of Semantic Web technologies promises intelligent retrieval and access to information through the use of semantic annotations based on relevant ontologies. In a scenario of technology monitoring, the automatic generation of semantic annotations of Web document has a significant role. In this article, after describing a new approach based on Semantic Web technologies for building a technology monitoring system, we present an ontology-based algorithm for automatic search and annotation of Web documents. This algorithm will be encapsulated by agents in a multi agent technology monitoring system.

This paper describes MEAT (Memory of Experiments for the Analysis of Transcriptomes), a project aiming at supporting biologists working on DNA microarrays. We provide methodological and software support to build an experiment memory for this domain. Our approach, based on Semantic Web Technologies, is relying on formalized ontologies and semantic annotations of scientific articles and other knowledge sources. It can probably be extended to other massive analyses of biological events.

This paper describes SAMOVAR (Systems Analysis of Modelling and Validation of Renault Automobiles), aiming at preserving and exploiting the memory of past projects in automobile design (in particular the memory of the problems encountered during a project) so as to exploit them in new projects. SAMOVAR relies on (1) the building of ontologies (in particular, thanks to the use of a linguistic tool on a textual corpus in order to enrich a core ontology in a semi-automatic way), (2) the «semantic» annotations of the descriptions of problems relatively to these ontologies, (3) the formalisation of the ontologies and annotations in RDF(S) so as to integrate in SAMOVAR the tool CORESE that enables an ontology-guided search in the base of the problem descriptions.

This article † is a survey of some methods, techniques and tools aimed at managing corporate knowledge from a corporate memory designer's perspective. In particular, it analyses problems and solutions related to the following steps: detection of needs of corporate memory, construction of the corporate memory, its diffusion (specially using the Internet technologies), use, evaluation and evolution