Search

The user-centred information retrieval needs to introduce semantics into the user modelling for a meaningful representation of user interests. The semantic representation of the user interests helps to improve the identification of the user's future cognitive needs. In this paper, we present a semantic-based approach for a personalised information retrieval. This approach is based on the design and the exploitation of a user profile to represent the user and his interests. In this user profile, we combine an ontological semantics issued from WordNet ontology, and a personal semantics issued from the different user interactions with the search system and with his social and situational contexts of his previous searches. The personal semantics considers the co-occurrence relations between relevant components of the user profile as semantic links. The user profile is used to improve two important phases of the information search process: (i) expansion of the initial user query and (ii) adaptation of the search results to the user interests. [ABSTRACT FROM AUTHOR]

Byline: Samir Kechid, Habiba Drias This paper proposes a new approach using a multi agents system for personalising the distributed information retrieval (DIR). Most prior research in DIR focused on selecting and merging information that has the most relevant content according to the query, but ignored the user's specific needs. Our approach extends the state-of-the-art in a DIR. Firstly, it develops an agent called user-agent for managing the user profile. Secondly, it develops an agent called source-agent for each information source in order to manage its information source. Finally, it develops an agent called agent-broker for cooperating between user-agent and each source-agent in order to select the best source and merge their best documents in response to the user's query. The approach has been experimented with several known information sources. The experimental results show that the approach: (1) improve the relevance of the result, (2) reduce the response times and (3) improve the system extensibility.

Cloud computing is a technology that provides users with a large storage space and an enormous computing power. However, the outsourced data are often sensitive and confidential, and hence must be encrypted before being outsourced. Consequently, classical search approaches have become obsolete and new approaches that are compatible with encrypted data have become a necessity. For privacy reasons, most of these approaches are based on the vector model which is a time consuming process since the entire index must be loaded and exploited during the search process given that the query vector must be compared with each document vector. To solve this problem, we propose a new method for constructing a secure inverted index using two key techniques, homomorphic encryption and the dummy documents technique. However, 1) homomorphic encryption generates very large ciphertexts which are thousands of times larger than their corresponding plaintexts, and 2) the dummy documents technique that enhances the index security produces lots of false positives in the search results. The proposed approach exploits the advantages of these two techniques by proposing two methods called the compressed table of encrypted scores and the double score formula. Moreover, we exploit a second secure inverted index in order to manage the users' access rights to the data. Finally, in order to validate our approach, we performed an experimental study using a data collection of one million documents. The experiments show that our approach is many times faster than any other approach based on the vector model. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a dynamic information foraging approach is proposed. Game theory and more precisely normal‐form is adapted to model the addressed problem. We tackle the issue of Web information foraging as a game played by a set of self‐interested agents that aim at reaching relevant Web pages in a short time. We introduce three kinds of agents along with the strategies followed by each one of them. In fact, the actions performed by the agents would highly depend on the strategy they adopt. We propose a pure strategy, a mixed strategy, and a fully mixed strategy respectively for the three kinds of agents. The agents share a common goal, which is satisfying a specific information need and work together in order to achieve it. The cooperative aspect between the self‐interested agents allows to get information from the Web in an effective and efficient way. Furthermore, we discuss the dynamic aspect of our information foraging approach which is ensured thanks to a modular multi‐agent system architecture. In order to test our Web information foraging system, we performed extensive experiments on the Citation Network Dataset that includes more than 2.3 million scientific publications. A preprocessing step was conducted with the aim of classifying the publications according to the 2012 ACM ontology, the purpose being to make our approach scalable. We also compared the results of our approach with two information access approaches. The first one deals with Web information retrieval whereas the second concerns medical information foraging. The experimental results show the superiority of our approach in terms of efficiency and results quality. [ABSTRACT FROM AUTHOR]