Recognition of Higher-Order Relations among Features in Textual Cases Using Random Indexing

Abstract

We envisage retrieval in textual case-based reasoning (TCBR) as an instance of abductive reasoning. The two main subtasks underlying abductive reasoning are ‘hypotheses generation’ where plausible case hypotheses are generated, and ‘hypothesis testing’ where the best hypothesis is selected among these in sequel. The central idea behind the presented two-stage retrieval model for TCBR is that recall relies on lexical equality of features in the cases while recognition requires mining higher order semantic relations among features. The proposed account of recognition relies on a special representation called random indexing, and applies a method that simultaneously performs an implicit dimension reduction and discovers higher order relations among features based on their meanings that can be learned incrementally. Hence, similarity assessment in recall is computationally less expensive and is applied on the whole case base while in recognition a computationally more expensive method is employed but only on the case hypotheses pool generated by recall. It is shown that the two-stage model gives promising results.