Knowledge Representation & Reasoning about Distances
Distance spaces - as models of closeness in space or similarity of objects - play a fundamental role in such diverse fields as geographic information systems, computational molecular biology, text processing, and data mining.
Complementing the existing database technology by means of knowledge representation methodologies is a promising approach to many of the challenging open problems in these and other fields, where sets of objects whose properties depend heavility on some notion of distance play an important role. In this project we will design representation and reasoning formalisms covering both quantitative and qualitative knowledge about distances. The project will combine work on logical and computational properties of the designed logics with work on tableau, resolution, and term-rewriting types of reasoning algorithms. The algorithms will be implemented and resulting systems will be used for initial experiments with representative case studies. An integration of the resulting languages with terminological languages (description logics) will be developed, implemented, and tested as well.