As the graph-based query in SPARQL query processing becames more and more inefficient due to the increasing structure complexity of Resource Description Framework (RDF) in the graph, by analyzing the basic structure of RDF graphs and the selectivity of the RDF vertices, RDF Triple Patterns Selectivity (RTPS) was proposed to improve the efficienccy of subgraph matching for graph with RDF, which is a graph structure segmentation rule based on selectivity of RDF vertices. Firstly, according the commonality of the predicate structure in the data graph and the query graph, an RDF Adjacent Predicate Path (RAPP) index was built, and the data graph structure was transformed into incoming-outgoing predicate path structure to determine the search space of query vertices and speed up the filtering of RDF vertices. Secondly, the model of Integer Linear Programming (ILP) problem was built to divide a RDF query graph with complicated structure into several query subgraphs with simple structure. By analyzing the structure characteristics of the RDF vertices in the adjacent subgraphs, the selectivity of the query vertices was established and the optimal segmentation method was determined. Thirdly, with the searching space narrowed down by the RDF vertex selectivity and structure characteristics of adjacent subgraphs, the matchable RDF vertices in the data graph were found. Finally, the RDF data graph was traversed to find the subgraphs whose structure matched the structure of query subgraphs. Then, the result graph was output by joining the subgraphs together. The controlling variable method was used in the experiment to compare the query response time of RTPS, RDF Subgraph Matiching (RSM), RDF-3X, GraSS and R3F. The experimental results show that, compared with the other four methods, when the number of triple patterns in a query graph is more than 9, RTPS has shorter query response time and higher query efficiency.

Widespread attentions have been drawn to Recommendation Systems (RS) as rapid development of social networks. To solve the cold-start problem and neglect to user's social network information in current recommendation algorithms, a novel Personalized Recommend Algorithm based on Graph Entropy (PRAGE) in trust social network was proposed. Firstly, a weighted User-Item Graph (UIG) was built based on feedback information, and a trust mechanism was introduced to establish a User Trust Graph (UTG). Secondly, by using random walk algorithm on two graphs, the initial similarity of user and item and new user-item similarity based on trust mechanism were obtained; the above algorithm process was repeated until the similarity value reaches convergence value. Then, a method to weight two sets of similarity values with graph entropies of both UIG and UTG was proposed and final recommendation list was accordingly created. The experimental results on two real-world datasets named as Epinions and FilmTrust reveal that, compared with classical Random Walk algorithm, the accuracy of PRAGE is increased by about 34.7%and 19.4% respectively, and its recall is increased by 28.9% and 21.1% respectively, which can alleviate cold start problem effectively and has better performance in accuracy and coverage.

With the complexity increasing of query graph structure, the efficiency of graph-based query in SPARQL query processing becomes lower and lower. By analyzing the basic structure of Resource Description Framework (RDF) graph, a subgraph matching method based on structure segmentation of query graph, called RSM (RDF Subgraph Matching), was proposed. Firstly, a query graph was divided into several simple query subgraphs, and query graph node searching space was defined through structure index of adjacent predicate. Secondly, the searching space was narrowed down by the adjacent subgraph structure, and a matchable subgraph could be found in data graph according to the searching area in the searching space. Finally, the result graph was output by joining related subgraphs. The query response times of RSM, RDF-3X, R3F and GraSS on query graphs with different structural complexity in different data sets were compared. The experimental results show that, compared with the other three methods, RSM has a shorter query response time and higher query efficiency in processing complex query graphs.