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.
In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (LexiconGrammar). Its role is to automatically feed the leaves with input data.
International audienceIn real applications, part of the data is usually missing. But most techniques of data analysis and data mining can only deal with complete data. In this paper, a new taxonomy of imputation methods is proposed. Within this taxonomy a new technique, based on entropy measures is introduced. Its behaviour is studied through an empirical comparative analysis
We have developed an early warning prototype, based on a knowledge management approach, so as to carry out the online detection of crises. Experts, with the help of automatic tools, design an ontology describing domain-specific crisis eruption processes. Then a recognition engine performs model-based inference, in order to identify among the events feeding the system, typical sequences that might trigger a crisis. Crises are described in the ontology through the template technique which provides also mechanisms to assess the similarity between stored scenarios and event flows related to the monitored process. This technique takes into account imperfect knowledge and uncertainties: for instance, imprecise temporal constraints between events are represented by fuzzy sets.
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