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This article provides a general discussion about how flexible querying can be applied to semistructured data (SSD). We adapt flexible querying ideas, already used for classically structured databases, to XQuery-like querying of SSD for managing users' priority and preferences, but also for tackling with the variability of SSD underlying structures. Indeed flexible querying seems to be still more useful for SSD than for classical databases, because of the potential structural heterogeneity of the former. Fuzzy sets are useful for expressing flexible requirements on attribute values and for estimating the degree of similarity of tags, or attribute labels, with elements present in the request. Priorities are introduced in the request for specifying the relative importance of elementary requirements in terms of their semantic contents, but also preferences about the location of information in the structure. The evaluation of the queries uses a qualitative scale with a finite number of levels, and retrieved pieces of SSD are rank-ordered using a lexicographic vector procedure. Illustrative examples are provided. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 723–737, 2007.

Queries to a database can be made more powerful by allowing flexibility in the specification of what has to be retrieved, and by referring to cases either for expressing the request, or for computing the answer. In this paper, we present an implemented information system (applied to a database describing houses to let), based on an approach developed in the fuzzy set and possibility theory setting. This provides a unified framework for expressing users' preferences about what they are looking for, for weighting the importance of requirements, for referring to examples that they like and/or counter-examples that they dislike, and for making case-based predictions. Thus information querying goes beyond the retrieving of items from a database, and involves associated tools which help the user to figure out the actual contents of the database.

Queries to a database can be made more powerful and user friendly by referring to cases, either for expressing the request, or for computing the answer. This requires some similarity-based reasoning facilities. In this paper, we present an implemented information system (applied to a database describing house renting), based on an approach developed in the fuzzy set and possibility theory setting. This provides a unified framework for: expressing user's preferences about what he is looking for; weighting the importance of requirements; expressing similarity relations; referring to examples that he/she likes and/or counter-examples that he/she dislikes; and for making case-based predictions.

The use of the Inside-Outside (IO) algorithm for the estimation of the probability distributions of Stochastic ContextFree Grammars is characterized by the use of all the derivations in the learning process. However, its application in real tasks for Language Modeling is restricted due to the that it needs to converge. Alternatively, several estimations algorithms which consider a certain subset of derivations in the estimation process have been proposed elsewhere. This set of derivations can be chosen according to structural criteria, or by selecting the k-best derivations. These alternatives are studied in this paper, and they are tested on the corpus of the Wall Street Journal processed in the Penn Treebank project.