This paper proposes a semi-automatic three step information scoring process that starts from constructs representing structured pieces of information and a user query. It first identifies the constructs relevant to answer the user question, based on their similarity to the query. The relevant items are then individually scored, taking into account both the reliability of their source and the certainty the latter expresses through its choice of linguistic terms. Lastly, these individual scores are fused, modeling a corroboration process that takes into account information obsolescence and source relations. This procedure is performed in the framework of possibility theory, relying on the definition of the appropriate aggregation operators.
Gradual itemsets of the form "the more/less A, the more/less B" summarise data through the description of their internal tendencies, identified as correlation between attribute values. This paper proposes to characterise gradual itemsets, enriching them with an additional clause introduced by the linguistic expression "especially if": they are of the form "the more/less A, the more/less B, especially if J ∈ R", where J is a set of attributes occurring in A ∪ B and R is a set of intervals defined for each attribute in J. The method proposed to automatically extract characterised gradual itemsets is based on appropriate mathematical morphology tools. The paper illustrates the relevance of the proposed approach on a real data set.
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