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  • × author_ss:"Tamine, L."
  • × theme_ss:"Retrievalalgorithmen"
  1. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.02 
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    Abstract
    A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.
    Date
    22. 3.2013 19:34:49
    Type
    a
  2. Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002) 0.00 
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    Abstract
    Boughanem, Chrisment, and Tamine use 144,186 documents and 25 queries from the TREC corpus AP88 to evaluate a genetic algorithm for multiple query evaluation against single query evaluation. They demonstrate niche construction by the use of a genetic technique to reproduce queries more often if they retrieve more relevant documents (genotypic sharing), or if they have close evaluation results (phenotypic sharing).New documents generated in each iteration are ranked by a merge based on one of these two principles. Genotypic sharing yields improvements of from 6% to 15% over single query evaluation, and phenotypic sharing shows from 5% to 15% improvement. Thus the niching technique appears to offer the possibility of successful merging of different query expressions.
    Type
    a

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