高阶逻辑下知识表示与聚类方法的研究

Higher-order logic-based knowledge representation and clustering algorithm

针对一阶逻辑在复杂结构数据环境中存在模式搜索空间庞大和不能发明新谓词的缺点,提出了使用类型化的高阶逻辑知识表示语言Escher去表示各种复杂结构的数据,利用其强类型语法有效地约束知识发现过程中模式的搜索空间和高阶的特点去解决新谓词构造的问题。设计了以Escher为基础的复杂结构数据中的知识发现过程和基于复杂结构数据的聚类算法,并以实验验证了其有效性。

The problems of predicate invention and utility are also difficult to solve and remain open problems in knowledge discovery based on first-order logic. Typed,higher-order logic knowledge representation formalism,Escher can express all kinds of complex structured data. It not only can provide strong guidance on the search for frequent patterns with its strong typed syntax,but also can resolve the problem of the invention of new predicates with its higher-order characteristic. It is fit for knowledge discovery in complex structured data. This paper investigated the knowledge discovery in complex structured data by employing Escher as knowledge representation formalism. In the case of algorithms,clustering of complex structured data was studied in it and experimental verification of its effectiveness.