Graph-index-based graph database keyword vicinity searching method

Abstract

The invention relates to the technical field of database keyword search, in particular to a graph-index-based graph database keyword vicinity searching method. The method comprises an off-line index constructing step and an on-line searching step, wherein the off-line index constructing step comprises the following steps of: defining a d-distance sub-graph for a weighted graph G=(V, E, w), generating d-distance sub-graphs which take certain nodes in the graphs as centers by an incomplete Dijkstra algorithm, storing the acquired largest d-distance sub-graph in a sub-graph base, constructing indexes from the nodes to the sub-graphs, designing a d-distance graph index structure, and storing the d-distance graph indexes in an interpreted mode; and the on-line searching step comprises the following steps of: searching in a combined sub-graph by a searching algorithm, and outputting a top-k answer. By zooming out a search target from an integral graph to a group of sub-graphs of limited sizes, the searching efficiency can be greatly improved.

Description

technical field [0001] The invention relates to the technical field of database keyword search, in particular to a graph index-based graph database keyword proximity search method. Background technique [0002] Keyword proximity search is a mainstream technology for keyword search of structured and semi-structured data in databases. Different from the traditional keyword search technology applied to unstructured data, such as the method of search engines such as Google, this technology does not only find a single document or object that contains all the given keywords, but searches in the database A structure that includes all keywords, and these structures are composed of some objects that are structurally related to each other, and as a whole can meet the information needs of users. It does not require users to master structured query languages ​​and complex database schemas, but also fully explores the potential value of structured information in the data in the database...

AI Technical Summary

Problems solved by technology

First of all, a Steiner graph is not a reasonable query answer, because it contains too much information, and compared with the minimum connection tree, users cannot accurately identify useful information from it. In addition, there are too many repeated information among Steiner graphs. None of the answers make sense

Secondly, there are still some technical problems that have not been solved, mainly including: the r-radius subgraph may be very large if there is no upper limit on the size, according to our investigation of actual data, when the subgraph is too large, the entire The method fails, and the query speed is slower; the simple graph index structure from keywords to subgraphs is relatively inefficient, because the query process also needs to know the nodes containing keywords, and the mapping relationship between nodes and subgraphs And other information, multiple index queries caused an increase in the number of disk I / O, which directly led to performance degradation (see Document 5)

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