A collaborative, semantic and context-aware search engine

Search engines help people to find information in the largest public knowledge system of the world: the Web. Unfortunately its size makes very complex to discover the right information. The users are faced lots of useless results forcing them to select one by one the most suitable. The new generation of search engines evolve from keyword-based indexing and classification to more sophisticated techniques considering the meaning, the context and the usage of information. We argue about the three key aspects: collaboration, geo-referencing and semantics. Collaboration distributes storage, processing and trust on a world-wide network of nodes running on users’ computers, getting rid of bottlenecks and central points of failures. The geo-referencing of catalogued resources allows contextualisation based on user position. Semantic analysis lets to increase the results relevance. In this paper, we expose the studies, the concepts and the solutions of a research project to introduce these three key features in a novel search engine architecture.213-21

A distributed software environment for collaborative web computing

Poster in the proceedingsThis paper describes an extensible core software element of a distributed, peer-to-peer system, which provides several facilities in order to help the implementation of collaborative, Web-based, distributed information storing and retrieval applications based on a decentralized P2P model. Moreover, after an architectural introduction of the core distributed software module, the Core Node, this paper describes a real application, named DART Node, based on it and designed and implemented within the DART (Distributed Agent-based Retrieval Tools) project, which carries out the idea of the design and implementation of a distributed, semantic and collaborative Web search engine, including mobile devices integration use cases.

Using SCXML to integrate semantic sensor information into context-aware user interfaces

This paper describes a novel architecture to introduce automatic annotation and processing of semantic sensor data within context-aware applications. Based on the well-known state-charts technologies, and represented using W3C SCXML language combined with Semantic Web technologies, our architecture is able to provide enriched higher-level semantic representations of user’s context. This capability to detect and model relevant user situations allows a seamless modeling of the actual interaction situation, which can be integrated during the design of multimodal user interfaces (also based on SCXML) for them to be adequately adapted. Therefore, the final result of this contribution can be described as a flexible context-aware SCXML-based architecture, suitable for both designing a wide range of multimodal context-aware user interfaces, and implementing the automatic enrichment of sensor data, making it available to the entire Semantic Sensor We

A DISTRIBUTED SOFTWARE ENVIRONMENT FOR COLLABORATIVE WEB COMPUTING

Abstract: This paper describes an extensible core software element of a distributed, peer-to-peer system, which provides several facilities in order to help the implementation of collaborative, Web-based, distributed information storing and retrieval applications based on a decentralized P2P model. Moreover, after an architectural introduction of the core distributed software module, the Core Node, this paper describes a real application, named DART Node, based on it and designed and implemented within the DART (Distributed Agent-based Retrieval Tools) project, which carries out the idea of the design and implementation of a distributed, semantic and collaborative Web search engine, including mobile devices integration use cases

A collaborative, semantic and context aware search engine

Search engines help people to find information in the largest public knowledge system of the world: the Web. Unfortunately its size makes very complex to discover the right information. The users are faced lots of useless results forcing them to select one by one the most suitable. The new generation of search engines evolve from keyword-based indexing and classification to more sophisticated techniques considering the meaning, the context and the usage of information. We argue about the three key aspects: collaboration, geo-referencing and semantics. Collaboration distributes storage, processing and trust on a world-wide network of nodes running on users' computers, getting rid of bottlenecks and central points of failures. The geo-referencing of catalogued resources allows contextualisation based on user position. Semantic analysis lets to increase the results relevance. In this paper, we expose the studies, the concepts and the solutions of a research project to introduce these three key features in a novel search engine architecture.</p