Research On Summarization Method Of Ontology In Semantic Web Of Things

The Semantic Web of Things introduces on the basis of leading semantic into the Internet of Things. One of the main purposes of constructing Semantic Web of Things is to get people to simply understand the ontology and be able to effectively operate ontology reasoning, therefore, ontology is a core of the Semantic Web of Things. However, since most of the ontology’s volume getting larger, for the demand of a particular user or a specific task, the solution resulting from the operation of redundant knowledge duplicate, inefficiency problem or other issues, is undoubtedly a huge challenge.Ontology summarization is the process of distilling knowledge from ontology to produce an abridged version for a particular user and task. Under the premise of retaining ontology’s original semantic, delete the redundant parts of the ontology to summary it. Not only reduce the ontology’s volume but also do not affect the final result of ontology reasoning, which can effectively solve the above problems caused by ontology’s large volume.Basing on some existing abroad researches on SWoT ontology and ontology summarization methods, this paper applies equivalence relation to summary ontology, and proposes the research framework; regarding Abox knowledge base as summarization object, by ontology parsing module, ontology Abox preprocessing module, ontology Abox summarization module, ontology reasoning module, design and implement SWoT ontology summarization system; test the system using the original ontology under two scenarios, namely user-defined ontology and the public ontology; contrast and analyze the summarization time, summarization rate and other aspects of the experimental ontology, and reason on the ontology before summarized and after summarized to verify the semantic equivalence property of the two. The experimental results show that the SWoT ontology summarization method based on equivalence relations not only retains the original semantic of ontology, but also reduces ontology’s volume, which makes more convenient for users to operate ontology, and it also do the theoretical and technical support for ontology reasoning, ontology reuse and other aspects of research.