基于描述逻辑的扩展预测模型标记语言EPMML  

Description Logic Based Extended Predictive Model Markup Language EPMML

作　　者：朱小栋[1,2] 肖芳雄[3] 黄志球[2] 沈国华[2] 靳玲[2] 

机构地区：[1]上海理工大学管理学院信息管理系,上海200093 [2]南京航空航天大学信息科学与技术学院,南京210016 [3]广西财经学院信息与统计学院,南宁530003

出　　处：《计算机学报》2012年第8期1644-1654,共11页Chinese Journal of Computers

基　　金：上海市教委科研创新项目基金(12YZ103);上海市优秀青年教师培养基金(slg10010);教育部人文社科青年基金(12YJC870037);上海市重点学科建设项目基金(S30504)资助~~

摘　　要：预测模型标记语言PMML正被许多数据挖掘组织作为标准化的数据挖掘模型描述语言.然而,由于数据挖掘技术的不断发展,参与建立PMML的数据挖掘厂商的经验有差异,PMML本身含有的大量语言元素不可避免地带来基于PMML的数据挖掘元数据的语义不一致问题.为解决这个问题,提出了一种基于描述逻辑的扩展预测模型标记语言EPMML,详细分析了EPMML的描述逻辑基础SOIN,设计EPMML的语言元素.基于EPMML描述的数据挖掘元数据可以转化为基于SOIN的知识库,进而进行知识推理以自动发现数据挖掘元数据的内在语义不一致问题.Racer推理实例验证了EPMML语言的良构性,良好表达能力和推理有效性.Predictive Model Markup Language PMML is currently used as standardized description language for data mining model by more and more DMG members. However, different experience of data mining products providers, constant development of data mining techniques, and PMML containing lots of language elements inevitably lead to inconsistency problems in PMML based data mining metadata. Considering this problem, a description logic SOIN is designed in this research. Its syntax and semantics are analyzed. An extended predictive model markup language EPMML is then proposed based on the SOIN infrastructure, the language elements are designed in detail. EPMML based data mining metadata can be transformed into knowledge base of SOIN, and then potential semantic inconsistency problems in the metadata can be automatically discovered by knowledge reasoning upon the knowledge base. Illustrations in the reasoning engineer Racer validate the well-formedness, well expressibility and reasoning efficiency of EPMML.

关 键 词：预测模型标记语言 数据挖掘 元数据 描述逻辑 知识推理 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]