User Oriented Information Retrieval in a Collaborative and Context Aware Search Engine

The web is the largest knowledge system who people can access. Unfortunately, its size makes very complex to find the right information. Search engines help people but they face user with a large amount of results, mostly useless, and he have to select one by one the right satisfying needs. To focus the attention on user requirements and involving the end-user in their systems, new search engines should evolve from keyword-based indexing and classification to more sophisticated techniques considering the meaning, the context and the usage of information. The key aspects are three: semantics, geo-referencing, collaboration. Semantic analysis lets to increase the results relevance. The geo-referencing of catalogued resources allows contextualisation based on user position. 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. In this paper, we describe the studies, the concepts and the solutions developed in the DART project to introduce these three key features in a novel search engine architecture. Key-Words: Search Engine, Geo-Reference, Semantics, NLP, RDHT, DART, 3D-UI, Community, P2P.