信息检索的概率模式匹配

Information retrieval for probabilistic schema matching

为解决不同的计算机平台、数据存储格式、文档模型以及结构文档模式的异构性,以及联邦数字图书馆和信息检索等应用环境中将一种模式下的数据结构转换成另一种模式下数据结构的需求。提出一个基于概率的模式匹配映射框架,称作PMap,使用概率论的方法,给出候选预测权值的概率学解释,从而选择一个最优的匹配方式。模式匹配就是寻找异构模式之间一致性,将主要应用在数据交换和联邦数字图书馆中的分布式信息检索领域中,使得异构文档获得统一的检索格式。

Distributed information systems tend to be highly heterogeneous, integrate different computer platforms, data storage formats, document models and schemas which structure the documents and the latter aspectrequires to transform data structured under one schema into data structured under a different schema. For these reason, a probabilistic framework is introduced, called PMap. Our approach gives a probabilistic interpretation of the prediction weights of the candidates, selects the rule set with highest matching probability. Schema matching is the problem of finding correspondences （mapping rules, e.g. logical formulae） between heterogeneous schemas e.g. in the data exchange domain, or for distributed IR in federated digital libraries. The union formulae is formed by IR heterogeneous.