Research On Key Technology Of RDF Data Query Optimization Based On Graph Model

Resource Description Framework(RDF)is a data model proposed by W3 C,which represents a main ingredient and data representation format for Linked Data and the Semantic Web.It supports a generic graph-based data model and data representation format for describing things,including their relationships with other things,and is an important data model for constructing knowledge graphs.SPARQL is a standard query language for RDF data proposed by W3 C.In general,the SPARQL query answering problem on RDF data can be turned into subgraph matching problems.RDF data can be modeled as RDF graphs and SPARQL queries can be modeled as basic graph patterns(BGP).Then we can easily find the query answer in the RDF graphs via subgraph matching.At present,there are two main problems in RDF data query answering based on graph model.The first is that the labels of vertices or edges in the RDF graph contain semantic information.In the subgraph matching process,if only consider the same labels and structure,some solution will be missed.The other is that most existing RDF data query answering techniques can not directly extend to handl continuous queries on dynamic graphs,and they only deal with one query at a time.However,due to the large-scale and evolving RDF data,it is more practical to use multiple queries to monitor and detect continuous patterns of interest.According to the above problem,this paper studies the optimization of RDF data query based on graph model,the main content includes the following two parts: 1)for the completeness of query results,due to the semantic characteristics of RDF data,we use rule-based reasoning to extend query answering based on RDFS and established rules.The obtained results are accurate and complete.In addition,we construct efficient abstrct graph indexes and optimization strategies to speed up query process;2)for the continuous multi-query optimization problem,we first extract the common features in the query set to speed up the query process,then construct an auxiliary data structure to track the intermediate results of each query.We further design an incremental maintenance strategy to efficiently deal with the updates of the data graph.Finally,a large number of experiments verify the effectiveness and efficiency of the proposed graph-based RDF data query optimization algorithms.