Research On Method Of Importance Computing Based Semantic Web Of Things Ontology Summarization

According to the intrinsic contradict of Internet of Things,introduce the process of semantic collaboration into Internet of Things to form a new generation network-Semantic Web of Things.Semantic collaboration process is to semantic annotation and semantic understanding based on ontology,hence,ontology is the core of Semantic Web of Things.But,Semantic Web of Things ontology mainly have two problems,the first problem is that the number of ontologies are too much and their have complex content,which is not benefit for ontology engineer's understanding and to support them to finish their job,such as ontology reuse.The second problem is the volume of ontologies are too large,regarding to the resource of the Semantic Web of Things bottom devices is limited,it is necessary to provide an ontology which is tidy and cover the most importance information of ontology.In order to solve the problem of the ontology is not benefit for people to understand and the large volume of ontologies,an importance computing-based Semantic Web of Things ontology summarization method is proposed in this paper,which can be ensure the process of semantic collaboration to be achieve better.This paper is based on structural and pragmatics features to realize the importance computing based Semantic Web of Things ontology summarization.Firstly,to pre-process the SWoT ontology,obtain term set,RDF sentence set and link type set,generate initiation RDF sentence sequence,create bipartite model to present the structure of ontology.Secondly,based on the bipartite model,to compute the structural importance of RDF sentence based on link analysis,and to compute the pragmatics importance of RDF sentence based on pragmatics statistics.Through the compare of statistics results and threshold to compute the combined importance of RDF sentence.Generate the RDF sequence which is sorted according to their importance,and design an algorithm to the process of importance computing.Finally,redundancy processing of the ranked RDF sentence sequence,extract the most importance object into summaries result set.After set the size of summaries,the result set is produced and designed an algorithm to the process of summaries generation.In order to verify the effectiveness of this method in the Semantic Web of Things,an importance computing-based Semantic Web of Things ontology summarization protosystem is designed and implemented in this paper.This system have three function modules,they are Semantic Web of Things ontology pre-processing modules,Semantic Web of Things ontology importance computing module and importance-based summaries generation module.These three modules mainly realize ontology parsing,modeling,importance computing,redundancy eliminating and summaries extracting.Finally the comparative experiences shows the accuracy and feasibility of this method,and the good performance of the protosystem.Some experimental results analyses show that this method can provide users with the best ontology summarization to meet their preferences.