Julien Gaugaz

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In contrast to classical databases and IR systems, real-world information systems have to deal increasingly with very vague and diverse structures for information management and storage that cannot be adequately handled yet. While current object-relational database systems require clear and unified data schemas, IR systems usually ignore the structured(More)
The rapidly increasing quantity and diversity of data stored on our PCs made locating information in this environment very difficult. Consequently, recent research has focussed on building semantically enhanced systems for either organizing or searching data on the desktop. Building on previous work, in this paper we present the Beagle toolbox, a set of(More)
The personal information stored on the desktop usually reaches huge dimensions nowadays. Its handling is even more difficult, taking into account complex environments and tasks we work with. An efficient method of identifying the present working context would mean an easier management of the needed resources. In this paper we propose a new way of(More)
Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this(More)
We present the Entity Name System (ENS), an enabling infrastructure, which can host descriptions of named entities and provide unique identifiers, on large-scale. In this way, it opens new perspectives to realize entity-oriented, rather than keyword-oriented, Web information systems. We describe the architecture and the functionality of the ENS, along with(More)
Detecting duplicate entities, usually by examining metadata, has been the focus of much recent work. Several methods try to identify duplicate entities, while focusing either on accuracy or on efficiency and speed - with still no perfect solution. We propose a combined layered approach for duplicate detection with the main advantage of using Crowdsourcing(More)
A good understanding of a user’s (working) contexts provides the basis for improved desktop information management, as well as for personalized desktop and Web search. We propose to combine a variety of evidences found by analyzing desktop information for inferring the user’s working contexts, and more specifically infer file-to-context assignment using a(More)
Existing desktop search systems, developed as a reaction to the rapidly increasing storage capacities of our hard disks, are an important step towards more efficient personal information management, yet they offer an incomplete solution. The industrial ones are capable of indexing a vast amount of file types, and the research prototypes can add very(More)