Fuzzy quantifiers like `almost all' and `about half' abound in natural language. They are used for describing uncertain facts, quantitative relations and processes. An implementation of these quantifiers can provide expressive and easy-to-use operators for aggregation and data fusion, but also for steering the fusion process on a higher level through a safe transfer of expert-knowledge expressed in natural language. However, existing approaches to fuzzy quantification are linguistically inconsistent in many common and relevant situations. To overcome their deficiencies, we developed a new framework for fuzzy quantification, DFS. We first present the axioms of the theory, intended to formalize the notion of `linguistic adequacy'. We then argue that the models of the theory are plausible from a linguistic perspective. We present three computational models and discuss some of their properties. Finally we provide an application example based on image data.
I. Glöckner and A. Knoll
Fuzzy Quantifiers: A Natural Language Technique for Data Fusion
Submitted to: Fourth International Conference on Information Fusion (Fusion 2001).