Informatin Integration on Emagent Semantics Systems

开发智能工具来集成从多个异构源提取的信息是一个具有挑战性的问题，以有效地利用全球信息系统中在线可用的大量动态源。聚合、概括或符号化的信息比原始信息更有助于理解复杂的现象。这种信息集成将通过专门针对网络信息系统定制的数据挖掘和信息提取的新方法来实现。在本研究中，我们提出了一种自适应数据挖掘系统作为特殊类型的信息系统。这些新颖的数据挖掘方法基于不同的方法，包括理论、构造和实验方法：（1）具有免疫记忆组织的免疫算法。（2）核方法，作为概率信息处理进行提升（3）传感器数据库中基于代理的分布式机器学习（4）基于复杂网络理论的图挖掘

Developing intelligent tools for the integration of information extracted from multiple heterogeneous sources is a challenging issue to effectively exploit the numerous and dynamic sources available on-line in global information systems. Aggregated, summarized or symbolized information is more useful for understanding complex phenomena than raw information. Such information integration will be brought by novel methods for data mining and information extraction specifically tailored to networked information systems.In this research we proposed an adaptive data mining system as the special kind of information system. These novel methods for data mining are based on different approaches, including theoretical, constructive, and experimental approaches :(1)immune algorithms with memory organization for immunity.(2)kernel methods, boosting as probabilistic information processing(3)agent based distributed machine learning in sensor databases(4)graph mining based on complex network theory