Fuzzy linguistic quantifiers – operators intended to model vague quantifying expressions in natural language like “almost all” or “few” – have gained importance as operators for information combination and data summarisation. They are particularly appealing because of their ease-of-use: people are familiar with these operators, which can be applied for technical aggregation purposes in the same...

1. I n t r o d u c t i o n Informally, a disposition is a proposition which is preponderantly, but not necessarily always, true. Simple examples of dispositions are: Smoking is addictive, exercise is good for your health, long sentences are more difficult to parse than short sentences, overeating causes obesity, Trudi is always right, etc. Dispositions play a central role in human reasoning, si...

In this paper, we briefly report our latest achievements in fuzzy granulation of OWL 2 ontologies. More precisely, we extend a previously presented method in order to address a new class of sentences with fuzzy quantifiers.

Ronald R. Yager Machine Intelligence Institute, Iona College New Rochelle, NY 10801 [email protected] (212)249-2047 ABSTRACT Fuzzy sets and other methods have been used to model a softening of constraints in CP problems. Here we suggest an approach to the softening of the CP problem at the meta level, the process used to aggregate the satisfactions to the individual constraints. We suggest the us...

Lindström [1] introduced a very powerful notion of quantifiers, which permits multi-place quantification and the simultaneous binding of several variables. ‘Branching’ quantifification was found to be useful by linguists e.g. for modelling reciprocal constructions like “Most men and most women admire each other”. Westerståhl [2] showed how to compute the three-place Lindström quantifier for “Q1...

Fuzzy quantifiers like “few”, “almost”, “about” and many others abound in natural language. They are used by humans for describing uncertain facts, quantitative relations and processes. An adequate contradiction-free computer-operational implementation of these quantifiers would provide a class of powerful yet human-understandable operators both for aggregation and fusion of data but also for s...

Proportional bounding quantifiers like “Between p1 and p2 percent” are potentially useful for expressing linguistic summaries of data. Given p1, p2, existing methods for data summarization based on fuzzy quantifiers can be used to assign a quality score to the summary. However, the problem remains how the optimal choice of p1, p2 in the range 0≤ p1 ≤ p2 ≤ 100% can be established. Moreover, the ...

Fuzzy quantification supplies powerful tools for handling linguistic expressions. Nevertheless, its advantages are usually shown at the theoretical level without a proper empirical validation. In this work, we review the application of fuzzy quantification in two application domains. We provide empirical evidence on the adequacy of fuzzy quantification to support different tasks in the context ...

Since quantifiers have the ability of summarizing the properties of a class of objects without enumerating them, linguistic quantification is a very important topic in the field of high level knowledge representation and reasoning. This paper introduces a new framework for modeling quantifiers in natural languages in which each linguistic quantifier is represented by a family of fuzzy measures,...