基于BTM的物联网服务发现方法

Service discovery method for Internet of Things based on Biterm topic model

针对物联网(IoT)服务描述文本篇幅较短、特征稀疏,直接采用传统的主题模型对IoT服务建模得到的聚类效果不佳,从而导致无法发现最佳服务的问题,提出了一种基于BTM的IoT服务发现方法。该方法首先利用BTM挖掘现有IoT服务的隐含主题,并通过全局主题分布和主题-词分布计算推理得到服务文档-主题概率分布;其次利用K-means算法对服务进行聚类,并返回服务请求的最佳匹配结果。实验结果分析表明,该方法能够有效提高IoT服务的聚类效果,从而得到匹配的最佳服务。与现有的HDP(Hierarchical Dirichlet Process)、基于K-means的隐狄利克雷分配(LDA-K)等方法相比,该方法进行最佳服务发现的准确度(Precision)和归一化折损累积增益(NDCG)均有一定幅度的提高。

Service description texts for Internet of Things(IoT)are short in length and sparse in text features,and direct modeling the IoT service by using traditional topic model has poor clustering effect,so that the best service cannot be discovered.To solve this problem,an IoT service discovery method based on Biterm Topic Model(BTM)was proposed.Firstly,BTM was employed to mine the latent topic of the existing IoT services,and the service document-topic probability distribution was calculated and deduced through global topic distribution and theme-word distribution.Then,K-means algorithm was used to cluster the services and return the best matching results of service requests.Experimental results show that the proposed method can improve the clustering effect of services for IoT and thus obtain the matched best service.Compared with the methods of HDP(Hierarchical Dirichlet Process)and LDA-K(Latent Dirichlet Allocation based on Kmeans),the proposed method achieves better performance in terms of Precision and Normalized Discounted Cumulative Gain(NDCG)for best service discovery.