网络信息搜索匹配算法研究与仿真

Study on Service Matchmaking Algorithm In Grid Enviroment

研究网络信息搜索问题,提高搜索匹配的准确率。当前网络资源中,信息资源种类繁多,数量巨大,拥有相似特征的信息资源很多,传统的针对资源特征匹配的算法,很难在巨大数量的拥有众多相似特征的网络资源信息中,准确找到需要匹配的资源信息,信息匹配的准确性不高。为了解决这一问题,提出了一种基于语义距离的服务相似度信息匹配方法,首先将数据集用本体语言描述出来,然后对所定义的信息量、本体中的连接路径进行形式化定义,确定两个概念之间的语义距离,进而进行匹配。实验表明,新算法是能够实现海量数据之间匹配的最佳信息搜索方案,摆脱传统方法对于特征的依赖。大幅提高了匹配的准确度,取得了不错的效果。

Research network information search to improve the accuracy of search matching.In the network resources,the information resources are various,and huge information resources have similar characteristics,thereofor,it is difficult for many traditional matching algorithms of resources characteristics to find the information resources needed to match,and the matching accuracy is not high.In order to solve this problem,this paper proposed a distance service based on semantic similarity information matching method.The method used body language to describe data set,then,the formalized definitions of the defined information,ontology path connection were done,the semantic distance between two concepts was determined.The experimental results show that the proposed algorithm is able to achieve the best match between mass data information search solutions.