基于ALCQ(D)的CBR事例表示及相似性度量

Research on CBR's Case Representation and Similarity Measure Based on ALCQ(D)

针对目前用于CBR事例表示的描述逻辑,如EL、ALC、ALCNR等缺少定性数量约束和有型域约束的问题,将具有定性数量约束和有型域构子的描述逻辑ALCQ(D)应用于CBR中。首先使用ALCQ(D)概念表示有定性数量约束、具体数据类型和数据值约束需求的CBR事例,并对之索引。研究两种主要的具体数据类型:数值类型和符号类型。然后定义ALCQ(D)范式来规范事例的索引表示,最后给出事例相似性度量方法。该度量方法先对事例索引的各个部分进行相似性度量,然后对度量结果进行加权求和得到最终相似性。实验结果表明,ALCQ(D)可以更准确地表示事例,事例相似性度量方法可以更贴切地度量事例的相似性,这对提高事例检索的速度和准确性以及提高CBR系统的效率具有重要意义。

Focused on the lack of qualified number restrictions and concrete domains restrictions in DLs such as （＆＆）,ALC,ALCNR that have been used in CBR＇s case representation,ALCQ（D） was used with which qualified number restrictions and concrete domains constructor were equipped.First,ALCQ（D） concepts were used to represent and index cases with the requirements of qualified number restrictions,concrete data types and numerical restrictions.Two concrete domain types which are numerical data type and symbolic data type were studied.Second,the normal form of ALCQ（D） was defined to normalize case representations in the form of indexes.Finally the measure method for case similarity was presented,which measures similarities of all parts of the case representations,then weights and summates gained similarities.Experimental results show that ALCQ（D） represents cases more accurately and the measure method for case similarity measures the similarity between cases more adequately.It is very important for increasing the speed of case retrieval,for improving the accuracy of case retrieval,and for improving the efficiency of the CBR system.