A new approach to applicable memory-based reasoning

To appropriately utilize the rapidly growing amount of data and information is a big challenge for people and organizations. Standard information retrieval methods, using sequential processing combined with syntax-based indexing and access methods, have not been able to adequately handle this problem. We are currently investigating a different approach, based on a combination of massive parallel processing with case-based (memory-based) reasoning methods. Given the problems of purely syntax-based retrieval methods, we suggest ways of incorporating general domain knowledge into memory-based reasoning. Our approach is related to the properties of the parallel processing microchip MS160, particularly targeted at fast information retrieval from very large data sets. Within this framework different memory-based methods are studied, differing in the type and representation of cases, and in the way that the retrieval methods are supported by explicit general domain knowledge. Cases can be explicitly stored information retrieval episodes, virtually stored abstractions linked to document records, or merely the document records themselves. General domain knowledge can be a multi-relational semantic network, a set of term dependencies and relevances, or compiled into a global similarity metric. This paper presents the general framework, discusses the core issues involved, and describes three different methods illustrated by examples from the domain of medical diagnosis.