Scientific Collaborations: principles of WikiBridge Design

Semantic wikis, wikis enhanced with Semantic Web technologies, are appropriate systems for community-authored knowledge models. They are particularly suitable for scientific collaboration. This paper details the design principles ofWikiBridge, a semantic wiki.Comment: in Adrian Paschke, Albert Burger begin_of_the_skype_highlighting end_of_the_skype_highlighting, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

Maariwa - ontologiebasiertes Webpublishing mit einem Semantic Wiki

Semantic Wikis dienen sowohl der kollaborativen Erstellung von Wissensrepräsentationen als auch der semantischen Annotation textueller Wiki-Inhalte. In dieser Arbeit wird ein Semantic Wiki Ansatz vorgestellt, der ein vereinfachtes, auf den Einsatz in einem Wiki zugeschnittenes Ontologiemetamodell mit einem auf aktuellen Browsertechnologien basierenden Bedienkonzept und einer einfachen semantischen Abfragesprache kombiniert. Textfragmente einer Wiki-Seite können interaktiv mit einer maschinenverwertbaren Semantik in einer für den Nutzer leicht verständlichen Art und Weise verknüpft werden, wobei der zusätzliche zur Annotierung zu leistende Aufwand minimiert wird. Durch ein einfaches und intuitives Bedienkonzept soll die Leistungsfähigkeit eines Semantic Wiki Systems auch für Anwenderkreise ohne Expertenwissen erschlossen werden

Un système multi-agents pour la gestion des connaissances hétérogènes et distribuées

La gestion des connaissances permet d'identifier et de capitaliser les savoirs faires de l'entreprise afin de les organiser et de les diffuser. Cette thèse propose un système de gestion des connaissances hétérogènes et distribuées, appelé OCEAN. Basé sur les ontologies et sur un système multi-agents, OCEAN a pour but de résoudre le problème de la capitalisation et de réutilisation des connaissances provenant de plusieurs sources différentes, afin d aider les acteurs métiers dans le processus de développement de produits mécaniques. Le système OCEAN repose sur un cycle de vie de quatre étapes Ce cycle de vie possède les phases : d identification, d extraction, de validation et se termine par la réutilisation des connaissances. Chaque phase constitue l objectif d une organisation d agents.L identification dans le système OCEAN consiste à définir les connaissances par un expert métier sous la forme d une ontologie. Les ontologies sont utilisées dans notre système pour représenter les connaissances définis d une façon structurée et formelle afin d être compréhensible par les machines. L extraction des connaissances dans OCEAN est réalisée par les agents de manière automatique à l aide des ontologies créées par les experts métiers. Les agents interagissent avec les différentes applications métiers via des services web. Le résultat de cette phase est stocké dans une mémoire organisationnelle. La validation des connaissances consiste à permettre aux acteurs métiers de valider les connaissances de la mémoire organisationnelle dans un wiki sémantique. Ce wiki permet de présenter les connaissances de la mémoire organisationnelle aux acteurs pour les réutiliser, les évaluer et les faire évoluer. La réutilisation des connaissances dans OCEAN est inspiré de travaux antérieurs intégrés au sein d OCEAN. Les quatre phases du cycle de vie des connaissances traitées dans cette thèse nous ont permis de réaliser un système apte à gérer les connaissances hétérogènes et distribuées dans une entreprise étendue.Among the goals of Knowledge Management we can cite the identification and capitalization of the know-how of companies in order to organize and disseminate them. This thesis proposes a heterogeneous and distributed knowledge management system, called OCEAN. Based on ontologies and multi-agents system, OCEAN aims to solve the problem of capitalization and reuse of multi-sources knowledge in order to assist business actors in the development process of mechanical products. The OCEAN system is based on a knowledge life cycle composed by four steps. This knowledge life cycle begins with the identification then extraction, validation and finishes with knowledge reuse. Each step is the goal of an organization of agents.The identification in OCEAN system consists in the definition of knowledge by a business expert with an ontology. Ontologies are used in our system to represent the knowledge, defined by the business expert, in a structured and formal way in order to be understandable by machines. Agents according to the ontology defined by business experts realize knowledge extraction in OCEAN automatically. Agents interact with professional softwares via web services. The result of this extraction is stored in an organizational memory (OM). Validation of knowledge in OCEAN relies on business actors that validate the knowledge of the OM in a semantic wiki. This wiki allows also the presentation of this knowledge to business actors in order to reuse, evaluate or evolve it. Previous works, integrated within OCEAN, inspires the knowledge reuse step. The four steps lifecycle discussed in this thesis has enabled us to achieve a system that can manage heterogeneous and distributed knowledge in an extended enterprise.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches