Discovery of Maximal Analogies between Stories

Abstract

Given two documents in the form of texts, we present a notion of maximal analogy representing a generalized event sequence of documents with a maximal set of events. They are intended to be used as extended indices of documents to automatically organize a document database from various viewpoints. The maximal analogy is defined so as to satisfy a certain consistency condition and a cost condition. Under the consistency condition, a term in an event sequence is generalized to more abstract term independently of its occurrence positions . The cost condition is introduced so that meaningless similarities between documents are never concluded. As the cost function is monotone, we can present an optimized bottom-up search procedure to discover a maximal analogy under an upper bound of cost. We also show some experimental results based on which we discuss a future plan.