Research On Association Rules Mining Based On RDF Data In The Semantic Web

In the past few years, as people on the semantic web and data mining are two areas of research interest has been growing, has aroused great concern mining association rules based on RDF data from a basic introduction to the theoretical study, by theory of the test and application layers and progressive, deepening. However, very few people do excavation work Semantic Web data itself, which is the Semantic Web data mining work. RDF data mining technology has also been applied to many areas, we believe that this study will give a number of specific areas of research activity has brought great benefits, such as clinical medicine and biology, in addition can also be applied to many other involves complex some areas of heterogeneous data.In this paper, by understanding the semantic web data mining, knowledge and technology, and statistical data processing, analysis Semantic Web data mining techniques specific applications in clinical and biomedical, and the problems in practical applications and solutions exist, include the following aspects:First, the theoretical basis, including RDF data model schema definition language RDFS, ontology language OWL, and SPARQL query language description. Then the data mining process, the user according to the extended syntax to generate transaction model based on mining the user has specified a specified excavation mode, transaction sets and the resulting association rules using traditional algorithms for processing. Next is the association rule mining in the biomedical field, this chapter will have to experiment to prove that our approach enables data to the Semantic Web-based transactional semantics becomes, ultimately these matters will have a high-quality set of association rules. Finally, the association rule mining expansion, as this article is concerned how the information in the real world in the form of triples that out, so the front of the main sections of the traditional association rules algorithm, in this chapter that will be extended to several other association rules algorithm.