#Deltagraph 7 windows 10 full

#Deltagraph 7 windows 10 full

Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph. However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for large-scale natural language knowledge graph query. Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem. In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Experiments show that our approach outperforms state-of-the-art subgraph search and subgraph matching algorithms by up to several orders of magnitude with respect to query processing time. These techniques for subgraph search also lead to an improved algorithm for subgraph matching. In this paper, we propose a new subgraph search algorithm using equivalences of vertices in order to reduce search space: (1) static equivalence of vertices in a query graph that leads to an efficient matching order of the vertices and (2) dynamic equivalence of candidate vertices in a data graph, which enables us to capture and remove redundancies in search space.

Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. A lot of efforts have been made to develop practical solutions for these problems. Subgraph query processing (also known as subgraph search) and subgraph matching are fundamental graph problems in many application domains. Our experimental studies demonstrate that (Tree+¢) has a compact index structure, achieves an order of magnitude better performance in index construc- tion, and most importantly, outperforms up-to-date graph- based indexing methods: gIndex and C-Tree, in graph con- tainment query processing. It has two im- plications: (1) the index construction by (Tree+¢) is ef- ficient, and (2) the graph containment query processing by (Tree+¢) is efficient. Our study verifies that (Tree+¢) is a better choice than graph for indexing purpose, denoted (Tree+¢ ¸Graph), to ad- dress the graph containment query problem. In order to achieve better pruning ability than existing graph-based indexing meth- ods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (¢) on demand, without a costly graph mining process beforehand.

We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature se- lection cost, and pruning power. In this pa- per, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mecha- nisms to reduce the overall query processing cost.

Given a graph database G, and a query graph q, the graph contain- ment query is to retrieve all graphs in G which contain q as subgraph(s). As a result, it is of special interest to process graph contain- ment queries effectively on large graph databases. Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs.